Courses

B. Tech. AI & DS  

 

Course structure of B. Tech. AI & DS IIT Patna

SemesterCourse CodeCourse nameL-T-P-CreditOffering Department
Semester I CE111 Engineering Drawing 1-0-3-5 Civil
EE101 Electrical Sciences 3-1-0-8 Electrical
HS103 Communicative English for Engineers 2-0.5-1-6 Humanities and Social Science
MA101 Mathematics I 3-1-0-8 Mathematics
ME110 Workshop-I 0-0-3-3 Mechanical
PH103 Physics –I 3-1-0-8 Physics
PH 110 Physics Laboratory 0-0-3-3 Physics
Total credits: 41 
Semester II CB102 & CE102 Biology and Environmental Studies 3-0-0-6 CB & CE
CH103 Introductory Chemistry 3-1-0-8 Chemistry
CH110 Chemistry Laboratory 0-0-3-3 Chemistry
CS102 Programming and Data Structures 3-0-0-6 CS
CS112 Programming and Data Structures Laboratory 0-0-3-3 CS
EE103 Basic Electronics Laboratory 0-0-3-3 EE
MA102 Mathematics –II 3-1-0-8 Mathematics
ME102 Engineering Mechanics 3-1-0-8 ME
Total credits: 45 
Semester III MA2XX Mathematical III 3-1-0-8 Mathematics
HS2XX HSS Elective – I 3-0-0-6 Humanities and Social Science
CS2XX Algorithms 3-0-0-6 CS
CS22X Algorithms Laboratory 0-0-3-3 CS
CS2XX Discrete Mathematics 3-0-0-6 CS
CS2XX Digital Systems 2-0-2-6 CS
CS2XX Optimization techniques 3-0-0-6 CS
CS2XX Software Lab/Tools 0-0-3-3 CS
Total credits: 44
Semester IV HS2XX HSS Elective – II 3-0-0-6 Humanities and Social Science
MA2XX Open Elective I (Prob. Theory and Random Processes) 3-0-0-6 Mathematics
CS2XX Computer Architecture 3-0-0-6 CS
CS2XX Computer Architecture Lab 0-0-3-3 CS
CS2XX Theory of computation 3-0-0-6 CS
CS2XX Database 3-0-0-6 CS
CS2XX Database Lab 0-0-3-3 CS
Total credits: 36
Semester V XX3XX Open Elective 3-0-0-6 Science/Engg.
CS341 Operating Systems 3-0-0-6 CS
CS342 Operating Systems Lab 0-0-3-3 CS
CS3XX Computer Network 3-0-0-6 CS
CS3XX Computer Network Lab 0-0-3-3 CS
CS3XX Deep Learning 3-0-0-6 CS
CS2XX Innovative Design Lab 0-0-3-3 CS
CS3XX Artificial Intelligence-II 3-0-0-6 CS
Total credits: 39
Semester VI HS3XX HSS Elective – III 3-0-0-6 Humanities and Social Science
CS3XX Advance Machine Learning 3-0-0-6 CS
CS3XX Bayesian Data Analysis 3-0-0-6 CS
CS3XX Programming for AI/ML 0-0-3-3 CS
CS3XX Computer Vision 3-0-0-6 CS
CS3XX Capstone Project-I 0-0-3-3 CS
Total credits: 30
Semester VII XX4XX Open Elective 3-0-0-6  
CS4XX Natural Language Processing 3-0-0-6 CS
CS4XX Bigdata Analytics 2-0-2-6 CS
CS4XX Elective – I 3-0-0-6 CS
CS4xx Elective – II 3-0-0-6 CS
CS4XX Capstone Project-II 0-0-3-3 CS
Total credits: 33
Semester VIII CS4XX Bigdata Security 2-0-2-6 CS
xx4XX Elective- III 3-0-0-6  
xx4XX Elective-IV 3-0-0-6  
CS4XX individual Project 3-0-0-6 CS
Total credits: 24
  Proposed Electives

Database & Data Mining
Introduction to Computational Topology
Geometric and Topological Modelling for Scientists and Engineers
Mobile Robotics
Cloud Computing
Statistical signal processing
Estimation and Detection
information theory and coding
Introduction to Network Science
Cryptography
High Performance Computing
Social Text Mining,
AI in Healthcare
Conversational AI
Discrete Differential Geometry
Computational Geometry
Topological Data Analysis
Planning Algorithms,
A Mathematical Introduction to Robotics
Advanced Signal Processing for AI and DS
Edge AI
Statistical signal processing,
Estimation and Detection,
Applications of artificial intelligence in Chemistry
Graph Representation Learning,
Advanced Network Science,
Distributed Machine Learning
Deep Learning for NLP
Conversational Artificial Intelligence,
Machine Translation,
Information Retrieval and Mining,
Sentiment and Emotion Analysis
Advanced Operating Systems
Signal Processing and Machine Learning for Data Science
Applied Time Series Analysis
Probability and Random Process
Applied Time Series Analysis
Reinforcement Learning
 

Total credits: 299        

Semester I

CE111Engineering Drawing1-0-3-5Civil

Geometrical construction of simple plane figure: Bisecting the line, draw perpendicular, parallel line, bisect angle, trisect angle, construct equatorial triangle, square, polygon, inscribed circle.

Free hand sketching: prerequisites for freehand sketching, sketching of regular and irregular figures.

Drawing scales: Engineering scale, graphical scale, plane scale, diagonal scale, comparative scale, scale of chord.

Orthographic projection: Principle of projection, method of projection, orthographic projection, plane of projection, first angle of projection, third angle of projection, reference line.

Projection of points, lines and plane: A point is situated in the first quadrant, point is situated in the second quadrant, point is situated in the third quadrant, point is situated in the fourth quadrant, projection of line parallel to both the plane, line contained by one or both the plane, line perpendicular to one of the plane, line inclined to one plane and parallel to other, line inclined to both the plane, true length of line.

Missing views: Drawing of missing front view of a solid, missing top view of solids, missing side view of solids, Orthographic projection of simple solid: Introduction, types of solid, projection of solid when axis perpendicular to HP, axis perpendicular to VP, axis parallel to both HP and VP, axis inclined to both HP and VP.

Orthographic projection of simple solid: Introduction, types of solid, projection of solid when axis perpendicular to HP, axis perpendicular to VP, axis parallel to both HP and VP, axis inclined to both HP and VP.

Text and Reference Books:

  • B. Agrawal and CM Agrawal, Engineering Drawing, Tata McGraw-Hill Publishing Company Limited, 2008.
  • D. A. Jolhe, Engineering Drawing, Tata McGraw-Hill Publishing Company Limited, 2006.
  • K. Venugopal, Engineering Drawing and Graphics, 2nd ed., New Age International, 1994.

 

EE101Electrical Sciences3-1-0-8Electrical

Circuit Analysis Techniques, Circuit elements, Simple RL and RC Circuits, Kirchhoff’s law, Nodal Analysis, Mesh Analysis, Linearity and Superposition, Source Transformations, Thevnin's and Norton's Theorems, Time Domain Response of RC, RL and RLC circuits, Sinusoidal Forcing Function, Phasor Relationship for R, L and C, Impedance and Admittance.

Semiconductor Diode, Zener Diode, Rectifier Circuits, Clipper, Clamper, Bipolar Junction Transistors, Transistor Biasing, Transistor Small Signal Analysis, Transistor Amplifier, Operational Amplifiers, Op-amp Equivalent Circuit, Practical Op-amp Circuits, DC Offset, Constant Gain Multiplier, Voltage Summing, Voltage Buffer, Controlled Sources, Instrumentation Circuits, Active Filters and Oscillators.

Number Systems, Logic Gates, Boolean Theorem, Algebraic Simplification, K-map, Combinatorial Circuits, Encoder, Decoder, Combinatorial Circuit Design, Introduction to Sequential Circuits.

Magnetic Circuits, Mutually Coupled Circuits, Transformers, Equivalent Circuit and Performance, Analysis of Three-Phase Circuits, Electromechanical Energy Conversion, Introduction to Rotating Machines.

  • C. K. Alexander and M. N. O. Sadiku, Fundamentals of Electric Circuits, 3rd Edition, McGraw-Hill, 2008.
  • W. H. Hayt and J. E. Kemmerly, Engineering Circuit Analysis, McGraw-Hill, 1993.
  • Donald A Neamen, Electronic Circuits; analysis and Design, 3rd Edition, Tata McGraw-Hill Publishing Company Limited.
  • Adel S. Sedra, Kenneth C. Smith, Microelectronic Circuits, 5th Edition, Oxford University Press, 2004.
  • R. L. Boylestad and L. Nashelsky, Electronic Devices and Circuit Theory, 6th Edition, PHI, 2001.
  • M. M. Mano, M. D. Ciletti, Digital Design, 4th Edition, Pearson Education, 2008.
  • Floyd and Jain, Digital Fundamentals, 8th Edition, Pearson.
  • A. E. Fitzgerald, C. Kingsley Jr. and S. D. Umans, Electric Machinery, 6th Edition, Tata McGraw-Hill, 2003.
  • D. P. Kothari and I. J. Nagrath, Electric Machines, 3rd Edition, McGraw-Hill, 2004.

 

HS103Communicative English for Engineers2-0.5-1-6HSS

In today’s ‘global village’, there are many who believe that ‘Communication is like breathing and life would cease to continue without it’. This particular course on communication skills imbibes the same and therefore, it aims to equip the students with getting the basics right of communication and presentation skills for academic and professional purposes. It is designed to help the second language learners acquire fluency in both spoken and written English to communicate information with clarity, precision and confidence especially in the professional sphere. It will introduce learners not only to the basic concepts in communication but also focus on providing them a hands-on experience of the same. It is hoped that after commanding the skills required in spoken and written English, learners will be able to express themselves more effectively.

The course will have ten units and shall focus on the following topics:

Unit 1: Language and Communication
What is Communication
Nature, Style and Process of Communication
Communication Barriers
Objectives and Importance of Communication
Formal and Informal Communication
Verbal and Non-Verbal Communication

Unit 2: English Language Remedial Skills
Construction of Sentences
Subject-Verb Agreement
Tenses
Active and Passive Voice
Direct and Indirect Speech
Common Errors

Unit 3: Oral Skills
Public Speaking
Dealing with lack of confidence
Making an Effective Presentation
Telephone Etiquette
Understanding GD
Why conduct a GD?
How to gear up for a GD?
Different Phases of GD

Unit 4: Listening Skills
Meaning of Listening
Different Types of Listening
Barriers to Listening and Methods to overcome them
Various strategies to develop effective Listening
Semantic Markers

Unit 5: Reading Skills
What is Reading?
Types of Reading
Reading Comprehension

Unit 6: Writing Skills
Business Correspondence
Element and Style of Writing
Report Writing
Notice, Agenda and Minutes

Unit 7: Interview Techniques
How to prepare for an Interview
An Interview

Text and Reference Books:

  • V. S. Kumar, P.K. Dutt and G. Rajeevan, A Course in Listening and Speaking-I, Foundation books, 2007.
  • V.Sasikumar, P.KiranmaiDutt, Geetha Rajeevan, "A Course in Listening and Speaking-II', Foundation books, 2007.
  • Rizvi, Ashraf, Effective Technical Communication, Tata McGraw Hill, 2005.
  • Nitin Bhatnagar and MamtaBhatnagar, 'Communicative English for Engineers and Professionals, Pearson, 2010.

 

MA101Mathematics I3-1-0-8Mathematics

Properties of real numbers. Sequences of real numbers, montone sequences, Cauchy sequences, divergent sequences. Series of real numbers, Cauchy’s criterion, tests for convergence. Limits of functions, continuous functions, uniform continuity, montoneand inverse functions. Differentiable functions, Rolle's theorem, mean value theorems and Taylor's theorem, power series. Riemann integration, fundamental theorem of integral calculus, improper integrals. Application to length, area, volume, surface area of revolution. Vector functions of one variable and their derivatives. Functions of several variables, partial derivatives, chain rule, gradient and directional derivative. Tangent planes and normals. Maxima, minima, saddle points, Lagrange multipliers, exact differentials. Repeated and multiple integrals with application to volume, surface area, moments of inertia. Change of variables. Vector fields, line and surface integrals. Green’s, Gauss’ and Stokes’ theorems and their applications.

Text Books:

  • G. B. Thomas and R. L. Finney, Calculus and Analytic Geometry, 6th Ed/9th Ed, Narosa/ Addison Wesley/ Pearson, 1985/ 1996.
  • T. M. Apostol, Calculus, Volume I, 2nd Ed, Wiley, 1967. T. M. Apostol, Calculus, Volume II, 2nd Ed, Wiley, 1969.

Reference Books:

  • R. G. Bartle and D. R. Sherbert, Introduction to Real Analysis, 5th Ed, Wiley, 1999.
  • J. Stewart, Calculus: Early Transcendentals, 5th Ed, Thomas Learning (Brooks/ Cole), Indian Reprint, 2003.

 

ME110Workshop-I0-0-3-3Mechanical

Sheet Metal Working:

Sheet material: GI sheets, aluminium, tin plate, copper, brass etc.; Tools: steel rule, Vernier calipers, micrometer, sheet metal gauge, scriber, divider, punches, chisels, hammers, snips, pliers, stakes etc.; operations: scribing, bending, shearing, punching etc.; Product development: hexagonal box with cap, funnel etc.

Pattern Making and Foundry Practice:

Pattern material: wood, cast iron, brass, aluminium, waxes etc.; Types of patterns: split, single piece, match plate etc.; Tools: cope, drag, core, core prints, shovel, riddle, rammer, trowel, slick, lifter, sprue pin, bellow, mallet, vent rod, furnace etc. Moulding sands: green sand, dry sand, loam sand, facing sand etc., Sand casting: Sand preparation, mould making, melting, pouring, and cleaning. Joining: Classifications of joining processes; Introduction to Arc welding processes; power source; electrodes; edge preparation by using tools bench vice, chisels, flat file, square file, half round file, round file, knife edge file, scrapers, hacksaws, try squares; cleaning of job, Job: lap and butt joints using manual arc welding.

Machining Centre:

Introduction to different machine tools; Working principle of lathe, milling, drilling etc.; Setting and preparation of job using lathe and milling; Performing different operations namely, straight turning, taper turning, knurling, thread cutting etc.; Introduction to dividing head, indexing, performing operation in milling using indexing mechanism.

CNC Centre:

Introduction to CNC machines; Fundamentals of CNC programming using G and M code; setting and operations of job using CNC lathe and milling, tool reference, work reference, tool offset, tool radius compensation.

Text and Reference Books:

  • H. Choudhury, H. Choudhary and N. Roy, Elements of Workshop Technology, vol. I,Mediapromoters and Publishers Pvt. Ltd., 2007.
  • W. A. J. Chapman, Workshop Technology, Part -1, 1st South Asian Edition, Viva Book Pvt Ltd., 1998.
  • P.N. Rao, Manufacturing Technology, Vol.1, 3rd Ed., Tata McGraw Hill PublishingCompany, 2009.
  • B.S. Pabla, M.Adithan, CNC machines,New Age International, 2012.
  • G. B. Thomas and R. L. Finney, Calculus and Analytic Geometry, 6th Ed/9th Ed, Narosa/Addison Wesley/Pearson, 1985/1996.
  • T. M. Apostol, Calculus, Volume I, 2nd Ed, Wiley, T. M. Apostol, Calculus, Volume II, 2nd Ed, Wiley, 1969/1967.

 

PH103Physics-I3–1–0–8PH

Orthogonal coordinate systems and frames of reference, conservative and non-conservative forces, work-energy theorem, potential energy and concept of equilibrium; Rotation about fixed axis, translational-rotational motion, vector nature of angular velocity, rigid body rotation and its applications, Euler's equations; Gyroscopic motion and its application; Accelerated frame of reference, centrifugal and Coriolis forces.

Harmonic oscillator, damped and forced oscillations, resonance, coupled oscillations, small oscillation, normal modes, longitudinal and transverse waves, wave equation, plane waves, phase velocity, superposition wave packets and group velocity, two- and three-dimensional waves.

Failure of classical concepts, Black body radiation, photo-electric effect, Compton effect, Davison and Germer's experiment, Frank-Hertz experiment, Bohr's theory, Sommerfeld's model, correspondence principle, Planck hypothesis, De Broglie's hypothesis, Hilbert space, observables, Dirac notation, principle of superposition, wave packets, phase and group velocities, probability & continuity equation, eigenvalues and Eigen functions, orthonormality, expectation values, uncertainty principle, postulates of Quantum Mechanics, Schrodinger equation & its applications to 1D potentials, field quantization, periodic potential wells: Kronig Penny model and origin of band gap.

Textbooks:

  • D. Kleppner and R. J. Kolenkow, An introduction to Mechanics, Tata McGraw-Hill, New Delhi, 2000.
  • David Morin, Introduction to Classical Mechanics, Cambridge University Press, NY, 2007.
  • Frank S. Crawford, Berkeley Physics Course Vol 3: Waves and Oscillations, McGraw Hill, 1966.
  • Eyvind H. Wichmann, Berkeley Physics Course Vol 4: Quantum physics, McGraw Hill, 1971.

Reference Books:

  • R. P. Feynman, R. B. Leighton and M. Sands, The Feynman Lecture in Physics, Vol I, Narosa Publishing House, New Delhi, 2009.
  • R. P. Feynman, R. B. Leighton and M. Sands, The Feynman Lecture in Physics, Vol III, Narosa Publishing House, New Delhi, 2009.
  • R. Eisberg and R. Resnick, Quantum Physics of atoms, molecules, solids, nuclei and particles, John Wiuley and Sons (Asia) Pvt. Ltd., Singapore, 2002.
  • A. J. Dekker, Solid State Physics, Macmillan Pub. India Ltd., New Delhi, 2009.
  • David J. Griffith, Introduction to Quantum Mechanics, Pearson Education Ltd, New Delhi, 2009.
  • B.H. Bransden& C.J. Joachain, Quantum Mechanics, Pearson Education Ltd, New Delhi, 2008.

 

PH110Physics Laboratory0-0-3-3PH

The list of experiments is as follows:

  • Instructions to Students
  • Introduction to Error Analysis

Ex 1 Decay of Current in A Capacitive Circuit

Ex 2 Q-Factor of an LCR Circuit

Ex 3 Study of Hall Effect

Ex 4 Speed of Sound in Air

Ex 5 ‘g’ by A Compound Pendulum

Ex 6 Speed of Light in Glass

Ex 7 Determination of e/m

Ex 8 Interference of Light: Newton’s Ring

Ex 9 Surface Tension of Water by Method of Capillary Ascent

Ex 10 Determination of Plank’s constant by Photoelectric Effect

NSS/NOS/CulturalNSS/NOS/CulturalP/NP

Semester II

CB102&CE102Biology and Environment Studies3-0-0-6CB & CE

Module 1 - Biology: 1. Cell – Structure and logic of optimization; 2. Blood – The following tissue – Basis and rationale; 3. Organs – Structure, function, interactions, failure; 4. Molecular basis of disorders – example: Diabetes; 5. Modern techniques of evaluations and corrections; 6. Open discussions – Feedback from students

Module 2 – Environmental Science / Studies: 1.Ecology and Sustainable Development – Ecosystems, Natural cycles, Biodiversity, Man and environment; 2. Water Resources – Hydrologic cycle and its components, Groundwater and surface water, Water quality; 3. Environmental Sanitation: Conventional and ecological sanitation; 4. Environmental Pollution and Control – Air, Water, Soil, Noise Pollution, Solid and Hazardous Waste, Biomedical Waste, E-waste: Sources, effect, treatment and control; 5. Environmental Legislations and Standards; 6.Current Environmental Issues: Greenhouse gases and global warming, Acid rain, Ozone layer depletion, Climate change

Text Books:

  • Any basic Biology Book of CBSE Curriculum at +2 Level/ E-text Books
  • Davis, M.L. and Masten,S.J., Principles of Environmental Engineering and Science,2nd Edition, McGraw-Hill, 2013.
  • Kaushik, A. and Kaushik, C.P., Perspectives in Environmental Studies, 4thEdition, New Age International, 2014.

Reference Books:

  • Botkin,D.B. and Keller,E.A., Environmental Science,8th Edition, Wiley, 2012.
  • Cunningham, W.P. and Cunningham, M.A., Environmental Science: A Global Concern, 13thEdition, McGraw-Hill, 2015
CH103Introductory Chemistry3-1-0-8Chemistry

PHYSICAL CHMEISTRY

Thermodynamics: The fundamental definition and concept, the zeroth and first law. Work, heat, energy and enthalpies. Second law: entropy, free energy and chemical potential. Change of Phase. Third law. Chemical equilibrium, Chemical kinetics: The rate of reaction, elementary reaction and chain reaction.

Electrochemistry: Conductance of solutions, equivalent and molar conductivities and its variation with concentration. Kohlrausch’s law-ionic mobilities, Transference number of ions. activities, application of Debye-Huckel theory. The Walden’s rule. Debye-Huckel-Onsager treatment. Electrochemical cells, Nernst equation. Application of EMF measurements. Liquid junction potential, commercial cells – the primary and secondary cells. Fuel cells.

INORGANIC CHEMISTRY

Coordination chemistry: ligand, nomenclature, isomerism, stereochemistry, valence bond, crystal field and molecular orbital theories. Bioinorganic chemistry: Trace elements in biology, heme and non-heme oxygen carriers, haemoglobin and myoglobin; organometallic chemistry.

ORGANIC CHEMISTRY
Stereo and regio-chemistry of organic compounds, conformers. Bioorganic chemistry: amino acids, peptides, proteins, enzymes, carbohydrates, nucleic acids and lipids. Modern techniques in structural elucidation of compounds (UV – Vis, IR, NMR). Solid phase synthesis and combinatorial chemistry. Green chemical processes.

Textbooks:

  • P. W. Atkins, Physical Chemistry, ELBS, 5th Ed, 1994.
  • J. O'M. Bockris and A. K. N. Reddy, Modern Electrochemistry, Vol. 1 and 2, Kluwer Academic, 2000.
  • K. L. Kapoor, A Textbook of Physical Chemistry, Macmillan India, 2nd Ed, 1986.
  • F. A. Cotton and G. Wilkinson, Advanced Inorganic Chemistry, Wiley Eastern Ltd, New Delhi, 3rd Ed, 1972 (reprint in 1998).
  • D. J. Shriver, P. W. Atkins and C. H. Langford, Inorganic Chemistry, ELBS, 2nd Ed, 1994.
  • S. H. Pine, Organic Chemistry, McGraw Hill, 5th Ed, 1987

Reference Books:

  • Levine, Physical Chemistry, McGraw Hill, 4th Ed, 1995.
  • J. E. Huheey, E. A. Keiter and R. L. Keiter, Inorganic Chemistry: Principle, structure and reactivity, Harper Collins, 4th Ed, 1993.
  • L. G. Wade Jr., Organic Chemistry, Prentice Hall, 1987
CH110Chemistry Laboratory0-0-3-3Chemistry

Estimation of metal ion: Determination of total hardness of water by EDTA titration. Experiments based on chromatography: Identification of a mixture containing two organic compounds by TLC. Experiments based on pH metry.: Determination of dissociation constant of weak acids by pH meter. Experiments based on conductivity measurement: Determination of amount of HCl by conductometric titration with NaOH. Synthesis and characterization of inorganic complexes: e.g. Mn(acac)3, Fe(acac)3, cis-bis(glycinato)copper (II) monohydrate and their characterization by m. p. IR etc. Synthesis and characterization of organic compounds: e.g. Dibenzylideneacetone. Kinetics: Acid catalyzed hydrolysis of methyl acetate. Verification of Beer-Lamberts law and determination of amount of iron present in a supplied solution. Experiments based on electro gravimetry and electroplating. Experiments based on magnetometry.

CS102Programming and Data Structures3-0-0-6CS

Introduction to digital computers; introduction to programming- variables, assignments; expressions; input/output; conditionals and branching; iteration; functions; recursion; arrays; introduction to pointers; structures; introduction to data-procedure encapsulation; dynamic allocation; linked structures; introduction to data structures stacks, queues and trees; time and space requirements.

References:

  • B. W. Kernighan and D. Ritchie, The C Programming Language, Prentice Hall of India (2nd Edition).
  • A. Kelley and I. Pohl, A Book on C, Pearson Education (4th Edition).
  • P.J. Deitel and H.M. Deitel , C How To Program, Pearson Education (7th Edition).
CS112Programing and Data Structures Laboratory0-0-3-3CS

Introduction to Unix commands; Introduction to program development tools- vi editor, GNU compiler, testing and debugging, etc.; Implementation of programs in C language.

EE103Basic Electronics Laboratory0-0-3-3EE

Experiments using diodes and bipolar junction transistor (BJT): design and analysis of half -wave and full-wave rectifiers, clipping circuits and Zener regulators, BJT characteristics and BJT amplifiers; experiments using operational amplifiers (op- amps): summing amplifier, comparator, precision rectifier, astable and mono stable multi vibrators and oscillators; experiments using logic gates: combinational circuits such as staircase switch, majority detector, equality detector, multiplexer and demultiplexer; experiments using flip-flops: sequential circuits such as non- overlapping pulse generator, ripple counter, synchronous counter, pulse counter and numerical display.

Reference Books:

  • A. P. Malvino, Electronic Principles. New Delhi: Tata McGraw-Hill, 1993.
  • R. A. Gayakwad, Op-Amps and Linear Integrated Circuits. New Delhi: Prentice Hall of India, 2002.
  • R.J. Tocci: Digital Systems; PHI, 6e, 2001.
MA102Mathematics-II3-1-0-8MA

Linear Algebra: Vector spaces (over the field of real and complex numbers). Systems of linear equations and their solutions. Matrices, determinants, rank and inverse. Linear transformations. Range space and rank, null space and nullity. Eigenvalues and eigenvectors. Similarity transformations. Diagonalization of Hermitian matrices. Bilinear and quadratic forms.

Ordinary Differential Equations: First order ordinary differential equations, exactness and integrating factors. Variation of parameters. Picard's iteration. Ordinary linear differential equations of n-th order, solutions of homogeneous and non-homogeneous equations. Operator method. Method of undetermined coefficients and variation of parameters.

Power series methods for solutions of ordinary differential equations. Legendre equation and Legendre polynomials, Bessel equation and Bessel functions of first and second kind. Systems of ordinary differential equations, phase plane, critical point, stability.

Textbooks:

  • K. Hoffman and R. Kunze, Linear Algebra, Prentice Hall, 1996.
  • T. M. Apostol, Calculus, Volume II, 2nd Ed, Wiley, 1969.
  • S. L. Ross, Differential Equations, 3rd Ed, Wiley, 1984.
  • E. A. Coddington, An Introduction to Ordinary Differential Equations, Prentice Hall, 1995.
  • W.E. Boyce and R.C. DiPrima, Elementary Differential Equations and Boundary Value Problems, 7th Ed, Wiley, 2001.

Reference Books:

  • E. Kreyszig, Advanced Engineering Mathematics, 9th Edition, Wiley, 2005.
ME102Engineering Mechanics3-1-0-8MA
  1. Rigid body statics: Equivalent force system. Equations of equilibrium, Freebody diagram, Reaction, Static indeterminacy.
  2. Structures: 2D truss, Method of joints, Method of section. Beam, Frame, types ofloading and supports, axial force, Bending moment, Shear force and Torque Diagrams for a member:
  3. Friction: Dry friction (static and kinetic), wedge friction, disk friction (thrust bearing), belt friction, square threaded screw, journal bearings, Wheel friction, Rolling resistance.
  4. Centroid and Moment of Inertia
  5. Virtual work and Energy method: Virtual Displacement, principle of virtual work, mechanical efficiency, work of a force/couple (springs etc.), Potential Energy and equilibrium, stability.
  6. Introduction to stress and strain: Definition of Stress, Normal and shear Stress. Relation between stress and strain, Cauchy formula.
  7. Stress in an axially loaded member,
  8. Stresses due to pure bending,
  9. Complementary shear stress,
  10. Stresses due to torsion in axi-symmetric sections:
  11. Two-dimension state of stress, Mohr’s circle representation, Principal stresses

Text and Reference books:

  • I. H. Shames, Engineering Mechanics: Statics and dynamics, 4th Ed, PHI, 2002.
  • F. P. Beer and E. R. Johnston, Vector Mechanics for Engineers, Vol I - Statics, 3rd Ed, TataMcGraw Hill, 2000.
  • J. L. Meriam and L. G. Kraige, Engineering Mechanics, Vol I - Statics, 5th Ed, John Wiley, 2002.
  • E.P. Popov, Engineering Mechanics of Solids, 2nd Ed, PHI, 1998.
  • F. P. Beer and E. R. Johnston, J.T. Dewolf, and D.F. Mazurek, Mechanics of Materials, 6th Ed, McGraw Hill Education (India) Pvt. Ltd., 2012.
NSS/NOS/CulturalNSS/NOS/CulturalP/NP 

Semester III

MA2XXMathematical Statistics3–0–0–6MA

Ordered Statistics, probability distributions of Sample Range, Minimum and Maximum Order Statistics. Random Sampling, Sampling distributions: Chi-square, T, F distributions.

Point Estimation: Sufficiency, Factorization theorem, Consistency, Moment method of estimation, Unbiased Estimation, Minimum Variance Unbiased Estimator and their properties, Rao-Cramer lower bound, Rao-Blackwellization, Fisher Information, Maximum Likelihood Estimator and properties, Criteria for evaluating estimators: Mean squared error.

Interval Estimation: Coverage Probabilities, Confidence level, Sample size determination, Shortest Length interval, Pivotal quantities, interval estimators for various distributions.

Testing of Hypotheses: Null and Alternative Hypotheses, Simple hypothesis, Composite hypothesis, Test Statistic, Critical region, Error Probabilities, Power Function, Level of Significance, Neyman-Pearson Lemma, One- and Two-Sided Tests for Mean, Variance and Proportions, One and Two Sample T-Test, Pooled T-Test, Paired T-Test, Chi-Square Test, Contingency Table Test, Maximum Likelihood Test, Duality between Confidence Intervals.

Bayesian Estimation: Prior and Posterior Distributions, Quadratic Loss Function, Posterior Mean, Bayes Estimates for well Known Distributions (Normal, Gamma, Exponential, Binomial, Poisson, Beta etc.)

Text Books:

  • Mathematical Statistics with applications, Kandethody M. Ramachandran, Chris P. Tsokos, Academic Press.
  • Hogg R.V. & Craig A.T. (1978): Introduction to Mathematical Statistics
  • Probability and Statistics in Engineering, William W. Hines, Douglas C. Montgomery, David M. Goldsman, John Wiley & Sons, Inc.

Reference Books:

  • Statistical Inference, G. Casella and R.L. Berger, Duxbury Advanced Series.
HS2XXHSS Elective – I3–0–0–6HSS
CS201Algorithms3–0–0–6CS

Asymptotic notations, introduction to complexity (time/space) analysis of algorithms. Basic introduction to algorithmic paradigms like divide and conquer, recursion, greedy, dynamic programming, etc. Searching: binary search trees, balanced binary search trees, AVL trees and red-black trees, B-trees, hashing. Priority queues, heaps, Interval trees. Sorting: quick sort, heap sort, merge sort, radix sort, bucket sort, counting sort, etc. and their analysis. Graph Algorithms: BFS, DFS, connected components, topological sort, minimum spanning trees, shortest paths, network flow. Reducibility between problems and NP-completeness: discussion of different NP-complete problems.

Books

  • M. A. Weiss, Data Structures and Problem-Solving Using Java, 2nd Ed, Addison-Wesley, 2002.
  • T. H. Cormen, C. E. Leiserson, R. L. Rivest and C. Stein, Introduction to Algorithms, MIT Press, 2001.
  • B. W. Kernighan and D. Ritchie, The C Programming Language, 2nd Ed, Prentice
  • Hall of India, 1988.
  • A. Aho, J. E. Hopcroft and J. D. Ullman, The Design and Analysis of Computer
  • Algorithms, Addison-Wesley, 1974.
  • S. Sahni, Data Structures, Algorithms and Applications in C++, McGraw-Hill, 2001.
  • M. T. Goodrich and R. Tamassia, Algorithm Design: Foundations, Analysis and Internet
  • Examples, John Wiley & Sons, 2001.
CS22XAlgorithms Laboratory0–0–3–3CS

The laboratory component will emphasize two areas: Implementation of algorithms covered in class: This will involve running the algorithms under varying input sets and measuring running times, use of different data structures for the same algorithm (wherever applicable) to see its effect on time and space, comparison of different algorithms for the same problem etc. Design of Algorithms: This will involve design and implementation of algorithms for problems not covered in class but related to topics covered in class. The exact set of algorithms to design and implement is to be decided by the instructor. In addition, there will be at least one significantly large design project involving some real-world application. An efficient design of the project should require the use of multiple data structures and a combination of different algorithms/techniques. The lab work can be carried out using any programming language.

CS2XXLinear Algebra for Data Science3–0–0–6CS

Vectors: addition, scalar multiplication, inner product. Linear functions: linear functions, Taylor approximation and regression model. Clustering: norm, distances, clustering, and the k-means algorithm. Linear independence: linear dependence, basis, orthonormal vectors. Matrices: matrix operations, inverse matrices, simultaneous linear equations, Eigenvalues, and eigenvectors Least squares: least square problem, least square data fitting; the Schur decomposition, spectral expansion, rank-1 expansions. Fundamental theorem of linear algebra, rank-nullity theorem, singular value decomposition. Painter style and motifs, bases for a large dimensional space. Gram-Schmidt algorithm, projection, least squares, data fitting. Data compression, simplification of complex models from structural engineering (reduced-order systems). Discrete Fourier series: diagonal matrices in Fourier basis, applications

Text Books:

  • Stephen Boyd and Lieven Vandenberghe, Introduction to Applied Linear Algebra: Vectors, Matrices, and Least Squares (Cambridge University Press, 3rd edition)
  • Gilbert Strang, Introduction to Linear Algebra (Wellesley Cambridge Press, 5th edition)
CS2XXComputer Architecture3–0–3–9CS

CPU - registers, instruction execution cycle, RTL interpretation of instructions, addressing modes, instruction set. Case study - instruction sets of some common CPUs; Assembly language programming for some processor; Data representation: signed number representation, fixed and floating-point representations, character representation. Computer arithmetic - integer addition and subtraction, ripple carry adder, carry look-ahead adder, etc. multiplication – shift-and-add, Booth multiplier, carry save multiplier, etc. Division - non-restoring and restoring techniques, floating point arithmetic; CPU control unit design: hardwired and micro-programmed design approaches, Case study - design of a simple hypothetical CPU; Pipelining: Basic concepts of pipelining, throughput and speedup, pipeline hazards; Memory organization: Memory interleaving, concept of hierarchical memory organization, cache memory, cache size vs block size, mapping functions, replacement algorithms, write policy; Peripheral devices and their characteristics: Input-output subsystems, I/O transfers - program controlled, interrupt driven and DMA, privileged and non-privileged instructions, software interrupts and exceptions. Programs and processes - role of interrupts in process state transitions.

CS2XXOptimization techniques3–0–0–6CS

Linear programming: Introduction and Problem formulation, Concept from Geometry, Geo-metrical aspects of LPP, Graphical solutions, Linear programming in standard form, Simplex, Big M and Two-Phase Methods, Revised simplex method, Special cases of LPP.

Duality theory: Dual simplex method, Sensitivity analysis of LP problem, Transportation, Assignment and travelling salesman problem.

Integer programming problems: Branch and bound method, Gomory cutting plane method for all integer and for mixed integer LPP.

Theory of games: saddle point, linear programming formulation of matrix games, two-person zero-sum games with and without saddle-points, pure and mixed strategies, graphical method of solution of a game, solution of a game by simplex method. Computational complexity of the Simplex algorithm, Karmarkar's algorithm for LPP. Acquaintance to softwares like TORA and MATLAB.

Text Books:

  • Hamdy A. Taha, Operations Research: An Introduction, Eighth edition, PHI, New Delhi (2007).
  • S. Chandra, Jayadeva, Aparna Mehra, Numerical Optimization with Applications, Narosa Publishing House (2009).
  • A. Ravindran, D.T. Phillips, J.J. Solberg, Operation Research, John Wiley and Sons, New York (2005).
  • M. S. Bazaraa, J. J. Jarvis and H. D. Sherali, Linear Programming and Network Flows, 3rd Edition, Wiley (2004).

Reference Books:

  • D. G. Luenberger, Linear and Nonlinear Programming, 2nd Edition, Kluwer, (2003).
  • S. A. Zenios (editor), Financial Optimization, Cambridge University Press (2002).
  • F. S. Hiller, G. J. Lieberman, Introduction to Operations Research, Eighth edition, McGraw Hill (2006).
CS2XXSoftware Lab/Tools0–0–3-3CS

Bash shell programming – basic concepts, expressions, decision making selections, repetition, special parameters - positional parameters, shift, argument validation, script examples.

Android Basics: Getting started with Android development, project folder structure, simple programming, running project, generating build/APK of the app from Android Studio

First application: Creating Android Project, Android Virtual Device Creation, set up debugging environment, Workspace set up for development, launching emulator, debugging on mobile devices. Basic UI design: Basics about Views, Layouts, Drawable Resources, input controls, Input Events etc. Understand the app idea and design user interface/wireframes of mobile app

Set up the mobile app development environment.

Semester IV

HS2XXHSS Elective-II3-0-0-6HSS
MA2XXProb Theory and Random Processes3-0-0-6MA

Algebra of sets, probability spaces, random variables, cumulative distribution functions, mathematical expectations, conditional probability and expectation, moments and inequalities, special discrete and continuous probability distributions, function of a random variable, random vectors and their distributions, convolutions, joint, marginal and conditional distributions, product moments, independence of random variables, bivariate distributions and properties, order statistics and their distributions, sampling distributions, Central Limit Theorem, strong law of large numbers, sequence of random variables, modes of convergence, distributions of the sample mean and the sample variance for a normal population, chi-square, t and F distributions, method of moments and maximum likelihood estimation, concepts of unbiasedness, criteria for choosing estimators, consistency and efficiency of estimates, confidence intervals, pivotal quantities, confidence intervals for proportions, simple and composite hypothesis, null and alternative hypotheses, types of error, level and size of tests, the most powerful test and Neyman - Pearson Fundamental Lemma, tests for one- and two-sample problems for normal populations, tests for proportions, likelihood ratio tests, chi-sqaure test for goodness of fit. discrete and continuous stochastic processes, markov chains, transition probability matrix, state spaces, classification of states, stationary distributions, ergodicity, poisson process, birth and death process. Introduction to reliability analysis: Application of Bayes theorem in real life problem; Reliability analysis of simple syste serial, parallel and combined systems; First order uncertainty and reliability analysis (FORM), First order second mom (FOSM) and Advanced FOSM methods; Applications of risk and reliability analysis in engineering systems.

Text / Reference Books:

  • Scheaffer, R. L., Mulekar, M. S. and McClave, J. T., (2011): Probability and statistics for Engineers, Fifth Edition, Broo Cole, Cengage Learning.
  • Ang, A. H-S., and Tang, W. H., (2006): Probability Concepts in Engineering, Volumes 1. John Wiley and Sons.
  • Halder, A and Mahadevan, S., (2000): Probability, Reliability and Statistical Methods in Engineering Design, John Wiley Sons.
  • Rao, S.S., (1992): Reliability-Based Design, McGraw Hill, Inc.
  • Harr, M.E., (1987): Reliability-Based Design in Civil Engineering. McGraw Hill, Inc.
  • Ang, A. H-S, and Tang, W. H., (1975): Probability Concepts in Engineering Planning and Design, Volumes 2. John Wiley and Sons
  • Benjamin, J., and Cornell. A., (1963): Probability, Statistics, and Decision for Civil Engineers. McGraw Hill.
CS3XXArtificial Intelligence3-0-0-6CS
  • Introduction, Motivation of the course
  • Problem Solving: Uninformed search, Informed search, Local Search,
  • Game Playing: Minmax, Alpha-Beta Pruning, Constraint Satisfaction Problems: Factor Graphs, Backtracking Search, Dynamic Ordering, Arc consistency
  • Knowledge, Reasoning and Planning: Propositional and Predicate Calculus, Semantic Nets, Automated Planning
  • Machine Learning: Learning from examples and analogy, Naive Bayes, Decision Tree, Introduction to Graphical Models (HMM, MEMM, CRF), Neural Networks
  • Application Topics: Introduction to NLP, Introduction to Fuzzy Sets and Logic

References:

  • S. Russel and P. Norvig. Artificial Intelligence: A Modern Approach (Third Edition), Prentice Hall, 2009
  • E. Rich and K. Knight, Artificial Intelligence, Addison Wesley, 1990
  • T. Mitchel, Machine Learning, McGraw-Hill, 1997

Journals and Conference Proceedings:

  • Artificial Intelligence, Machine Learning, ACL Anthology, ICML, ECML etc.
CS2XXArtificial Intelligence Lab0-0-3-3CS

Small projects based on the concepts and tools taught in AI class.

CS3XXDatabase3-0-0-6CS

Database system architecture: Data Abstraction, Data Independence, Data Definition and Data Manipulation Languages; Data models: Entity-relationship, network, relational and object oriented data models, integrity constraints and data manipulation operations; Relational query languages: Relational algebra, tuple and domain relational calculus, SQL and QBE; Relational database design: Domain and data dependency, Armstrongs axioms, normal forms, dependency preservation, lossless design; Query processing and optimization: Evaluation of relational algebra expressions, query equivalence, join strategies, query optimization algorithms; Storage strategies: Indices, B-trees, hashing; Transaction processing: Recovery and concurrency control, locking and timestamp based schedulers, multiversion and optimistic Concurrency Control schemes; Recent Trends: XML Data, XML Schema, JSON and “NoSQL Systems, etc.,.

Books:

  • Abraham Silberschatz, Henry Korth, and S. Sudarshan, Database System Concepts, McGraw-Hill.
  • Raghu Ramakrishnan, Database Management Systems, WCB/McGraw-Hill.
  • Bipin Desai, An Introduction to Database Systems, Galgotia.
  • J. D. Ullman, Principles of Database Systems, Galgotia.
  • R. Elmasri and S. Navathe, Fundamentals of Database Systems, Addison-Wesley.
  • Serge Abiteboul, Richard Hull and Victor Vianu, Foundations of Databases. Addison-Wesley
CS34XDatabase Lab0-0-3-3CS

Database schema design, database creation, SQL programming and report generation using a commercial RDBMS like ORACLE/SYBASE/DB2/SQL-Server/INFORMIX. Students are to be exposed to front end development tools, ODBC and CORBA calls from application Programs, internet-based access to databases and database administration.

CS2XXTheory of computation3-0-0-6CS

Regular Languages: Finite Automata-Deterministic and Nondeterministic, regular operations, Regular Expressions, Equivalence of DFA, NFA and Res, Nonregular Languages and pumping lemma

Context-Free Languages: Context-Free Grammars, Chomsky Normal Form, Pushdown Automata, Non-Context-Free Languages and pumping lemma, Deterministic Context-Free Languages

Turing Machines: Definition of TM and its variants, Decidability, Reducibility.

Complexity Theory: Time complexity and Space Complexity.

Text Books:

  • Introduction to the Theory of Computation, by Michael Sipser,
  • Computational Complexity, by Christos H. Papadimitriou, Addison-Wesley publishers.
  • Computational Complexity: A Modern Approach, by Sanjeev Arora and Boaz Barak.
CS2XXMachine Learning & DS3-0-0-6CS

Supervised learning: decision trees, nearest neighbor classifiers, generative classifiers like naive Bayes, linear discriminate analysis, Support vector Machines, feature selection techniques: wrapper and filter approaches, back-ward selection algorithms, forward selection algorithms, PCA, LDA

Unsupervised learning: K-means, hierarchical, EM, K-medoid, DB-Scan, cluster validity indices, similarity measures, some modern techniques of clustering

Graphical models: HMM, CRF, MEMM

Semi-supervised learning

Primary books

  1. Pattern recognition and machine learning by Christopher Bishop, Springer Verlag, 2006.
  2. Hastie, Tibshirani, Friedman the elements of Statistical Learning Springer Verlag
  3. T. Mitchell. Machine Learning. McGraw-Hill, 1997.

Supplementary books

  1. Probability, Random Variables and Stochastic processes by Papoulis and Pillai, 4th Edition, Tata McGraw Hill Edition.
  2. Linear Algebra and Its Applications by Gilbert Strand. Thompson Books.
  3. Data Mining: Concepts and Techniques by Jiawei Han, Micheline Kamber, Morgan Kaufmann Publishers.
  4. A. K. Jain and R. C. Dubes. Algorithms for Clustering Data. Prentice Hall, 1988.

Semester V

XX3XXOpen Elective-III3-0-0-6
CS34XOperating Systems3-0-0-6CS

Process Management: process; thread; scheduling. Concurrency: mutual exclusion; synchronization; semaphores; monitors; Deadlocks: characterization; prevention; avoidance; detection. Memory Management: allocation; hardware sup- port; paging; segmentation. Virtual Memory: demand paging; replacement; allocation; thrashing. File Systems and Imple- mentation. Secondary Storage: disk structure; disk scheduling; disk management. (Linux will be used as a running example, while examples will drawn also from Windows NT/7/8.); Advanced Topics: Distributed Systems. Security. Real-Time Systems.

Books:

  • A. Silberschatz, P. B. Galvin and G. Gagne, Operating System Concepts, 8th Ed, John Wiley & Sons, 2010.
  • A. S. Tenenbaum, Modern Operating Systems, 2nd Ed, Prentice Hall of India, 2001.
  • H. M. Deitel, P. J. Deitel and D. R. Choffness, Operating Systems, 3rd Ed, Prentice Hall, 2004.
  • W. Stallings, Operating Systems: Internal and Design Principles, 5th Ed, Prentice Hall, 2005.
  • M. J. Bach, The Design of the UNIX Operating System, Prentice Hall of India, 1994.
  • M. K. McKusick et al, The Design and Implementation of the 4.4 BSD Operating System, Addison Wesley, 1996.
CS342Operating Systems Lab0–0–3-3CS

Programming assignments to build different parts of an OS kernel.

CS3XXComputer Network3-0-0-6CS

Evolution of computer networks; Physical Layer: Theoretical basis for data communication, transmission media and impairments, switching systems Medium Access Control Sublayer: Channel allocation Problem, multiple access protocols, Ethernet Data link layer: Framing, HDLC, PPP, sliding window protocols, error detection and correction Network Layer: Internet addressing, IP, ARP, ICMP, CIDR, routing algorithms (RIP, OSPF, BGP); Transport Layer: UDP, TCP, flow control, congestion control; Introduction to quality of service; Application Layer: DNS, Web, email, authentication, encryption.

Books:

  • Peterson & Davie, Computer Networks, A Systems Approach: 5th Edition
  • William Stallings Data and Computer Communication, Prentice Hall of India.
  • Behrouz A. Forouzan, Data Communication and Networking, McGraw-Hill.
  • Andrew S. Tanenbaum, Computer Networks, Prentice Hall.
  • Douglas Comer, Internetworking with TCP/IP, Volume 1, Prentice Hall of India.
  • W. Richard Stevens, TCP/IP Illustrated, Volume 1, Addison-Wesley.
CS3XXComputer Network Lab0-0-3-3CS

Simulation experiments for protocol performance, configuring, testing and measuring network devices and parameters/policies; network management experiments; Exercises in network programming.

CS3XXDeep Learning3-0-0-6CS

This course will provide basic understanding of deep learning and how to solve classification problems having large amount of data. In this course several public domain tools will be demonstrated to build deep learning network. Course content will be as follows: Brief introduction of big data problem, Overview of linear algebra, probability, numerical computation

  • Scalars, vectors, matrix, tensors, norms, Eigen value, eigenvector, singular value decomposition, determinant
  • Probability distribution, Bayes rule, conditional probability, variance, covariance
  • Overflow, underflow, gradient based optimization, least square.

--Neural network - Perceptron, Multi-level perceptron, Universal approximation theorem

--Tutorial for Tools

  • Keras, Theano, TensorFlow
  • Demo using MNIST

-- Deep learning network

  • Shallow vs Deep network
  • Deep feedforward network
  • Gradient based learning - Cost function, soft max, sigmoid function
  • Hidden unit - ReLU, Logistic sigmoid, hyperbolic tangent
  • Architecture design
  • Back propagation algorithm - Chain rule of calculus
  • SGD

-- Regularization - parameter norm penalties, drop out, noise robustness, early stopping, Batch normalization

-- Optimization for training deep model- Adagrad, Nesterov momentum

-- Advanced topics

  • Convolutional Neural Network
  • Recurrent Neural Network/ Sequence modeling

-- Practical applications - MNIST, etc.

Books

  • Ian Goodfellow, Yoshua Bengio and Aaron Courville, “Deep Learning”
  • Richard S. Sutton & Andrew G. Barto, Reinforcement Learning: An Introduction” (available online)
  • Jerome H. Friedman, Robert Tibshirani, and Trevor Hastie, “The elements of statistical learning”
CS2XXInnovative Design Lab0-0-3-3CS

The objective of this lab would be to encourage and provide support to students for some innovative work. The work may focus on inventing a practical solution for a pure Computer Science or multidisciplinary problems. Depending on the nature of the work, it may be carried out in a group or individual mode.

CS3XXArtificial Intelligence-II3-0-0-6CSE
  1. Introduction to the course
  2. Knowledge Representation: Ontology, Knowledge Graph, Semantic Web3, Uncertain Knowledge and Reasoning: Quantifying uncertainty, Probabilistic Reasoning, Probabilistic Reasoning over time, Multi-agent decision making
  3. Markov Decision Processes: Policy evaluation, Policy improvement, Policy iteration, Value iteration
  4. Re-inforcement Learning: Monte Carlo, SARSA, Q-learning, Exploration/Exploitation, Function approximation, Deep re-inforcement learning
  5. Evolutionary Computation: Genetic Algorithm, Ant Colony Optimization, Particle Swarm Optimization, Differential Evolution
  6. Conversational AI, Explainable AI, Understanding AI Ethics and Safety

References:

  1. S. Russel and P. Norvig. Artificial Intelligence: A Modern Approach (Third Edition), Prentice Hall, 2009
  2. E. Rich and K. Knight, Artificial Intelligence, Addison Wesley, 1990
  3. Ian Goodfellow, Yoshua Bengio and Aaron Courville, Deep Learnng, MIT Press, 2016
  4. Daphne Koller and Nir Friedman, Probabilistic Graphical Models: Principles and Techniques, MIT Press, 2009.
  5. Sutton and Barto. Reinforcement Learning: An Introduction. Available free online.
  6. Hastie, Tibshirani, and Friedman. The elements of statistical learning. Available free online.

Journals and Conference Proceedings:

  • Artificial Intelligence, Machine Learning, ACL Anthology, COLING, ICML, ECML, Proceedings of Uncertainty in AI, ICCV, ICLR etc.

Semester VI

HS3XXHSS Elective-III3-0-0-6HSS
CS3XXAdvance Machine Learning3-0-0-6CS

Mathematics of machine learning,

Overview of supervised, unsupervised learning and Multi-task learning

  • Undirected graphical models: Undirected graphical models: overview, representation of probability distribution and conditional independence statement, Factorization, CRFs, Applications to NLP, Markov networks.
  • Directed graphical models: Bayesian networks.
  • Deep Networks for Sequence Prediction: Encoder-decoder models (case study translation), Attention models, LSTM, Memory Networks
  • Deep Network for Generation – Sequence to Sequence Models – Variational Auto encoders – Generative Adversarial Networks (GANs) – Pointer Generator Networks – Transformer Networks

Learning Representations – Learning representations for text – Word2Vec, FastText, GLOVE, BERT – Learning representations in images based on context prediction (C. Doersch et al. Unsupervised Visual Representation Learning by Context Prediction, ICCV 2015)

Time series forecasting: models and case-studies

Modern clustering techniques: Multi-objective optimization for clustering, Deep learning for clustering Online Learning, Mistake Bounds, Sub-space clustering

Meta-learning and federated learning

Case-studies: Recent topics for solving various problems of natural language processing, bioinformatics, information retrieval

Books:

  • Kevin P. Murphy. Machine Learning: A Probabilistic Perspective. MIT Press 2012
  • Ian Goodfellow, Yoshua Bengio and Aaron Courville. Deep Learning. MIT Press 2016

Other relevant textbooks:

  • Yoav Goldberg. 2016. A primer on neural network models for natural language processing. J. Artif. Int. Res. 57, 1 (September 2016), 345-420.
  • R. G. Cowell, A. P. Dawid, S. L. Lauritzen and D. J. Spiegelhalter. "Probabilistic Networks and Expert Systems". Springer-Verlag. 1999.
  • M. I. Jordan (ed). "Learning in Graphical Models". MIT Press. 1998.
CS3XXBayesian Data Analysis3-0-0-6CS

Introduction: Objective vs Subjective Definition of Probability, Axiomatic Definition of Probability, Bayes Theorem Applications of Bayes Theorem

Decision Theoretic framework and major concepts of Bayesian Analysis Likelihood, Prior and posterior, Loss function, Bayes Rule, Conjugate priors and other priors, Sensitivity Analysis, Posterior Convergence, One-parameter Bayesian models, Poisson Model for Count data, Binomial Model for Count data, Multi-parameter Bayesian models, Univariate Gaussian Model, Multivariate Gaussian Model, Covariance Matrix with Wishart Distribution

Bayesian solution for high-dimensional problem in Covariance matrix for Portfolio Risk Analysis

Multinomial-Dirichlet Allocation Models for Topic Model

Bayesian Machine Learning, Hierarchical Bayesian Model

Regression with Ridge prior, LASSO prior, Classification with Bayesian Logistic Regression, Discriminant Analysis

Bayesian Computation with Stan

Estimation of Posterior Mode with Optimization

Estimation of Posterior Mean and other summary with Monte Carlo Simulation

Accept-Rejection Sampling

Importance Sampling

Markov Chain and Monte Carlo

Metropolis-Hastings

Hamiltonian Monte Carlo

Gaussian Process Regression

Introduction

Gaussian Process Regression for Big Data

Bayesian Optimization

Textbook:

  • John Kruschke: Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan (2014), Academic Press
  • Carl Edward Rasmussen and Christopher K. I. Williams: Gaussian Processes for Machine Learning, MIT Press (2006) Available Online
  • Sourish Das, Sasanka Roy, Rajiv Sambasivan : Fast Gaussian Process Regression for Big Data, Big Data Research, Volume 14, December 2018, Pages 12-26: Preprint Available Here; Python Implementation
CS3XXProgramming for AI/ML0-0-3-3CS

Programming assignments based on tools and techniques taught in ML/DL/AI-II courses. Prolog; Assignment on Logistic regression; Assignment on k-means clustering.

Introduction to Tensorflow, Pytorch, Keras.

Usage of Tensorflow, Pytorch and/or Keras: Simple ML examples; Assignments on NNs; Assignments on CNNs; Assignments on RNN; Assignment on LSTM, GRU

References:

  • Pytorch: https://pytorch.org/assets/deep-learning/Deep-Learning-with-PyTorch.pdf
  • First Contact with TensorFlow: Get Started with Deep Learning Programming by Jordi Torres
  • https://analyticsindiamag.com/top-10-free-books-and-resources-for-learning-tensorflow/
  • https://keras.io/getting_started/learning_resources/
  • Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow (second edition), by Aurélien Géron
CS3XXComputer Vision3-0-0-6CS

The course will have a comprehensive coverage of theory and computation related to imaging geometry, and scene understanding. It will also provide exposure to clustering, classification and deep learning techniques applied in this area. Camera geometry, Stereo geometry, Stereo Geometry, Feature detection and description Feature matching and model fitting, Color Processing, Range image processing Clustering and classification, Dimensionality Reduction and Sparse Representation Deep Neural Architecture and applications.

CS4XXCapstone Project-I0-0-3-3CS

The objective of this project would be to encourage and provide support to students for some innovative work. The work may focus on inventing a practical solution for a AI/DS or multidisciplinary problems. Depending on the nature of the work, it may be carried out in a group or individual mode.

Semester VII

XX4XXOpen Elective3-0-0-6Science/ Engineering Deptt.
CS4XXNatural Language Processing3-0-0-6CS

Course Contents:

Intro to NLP

Simple Word Vector representations: word2vec, GloVe: Distributed Representations of Words and Phrases and their Compositionality, [Efficient Estimation of Word Representations in Vector Space

Advanced word vector representations: language models, GloVe: Global Vectors for Word Representation

PoS tagging and named entity recognition

Language modeling and other tasks, Opinion Mining

Parsing, Sentence classification

Machine Translation,

Dynamic Memory Networks

Question Answering, Natural Language Generation and Summarization

Contextual Word Representations: BERT

Text and References:

  • Dan Jurafsky and James H. Martin. Speech and Language Processing (3rd ed. draft)
  • Jacob Eisenstein. Natural Language Processing
  • Yoav Goldberg. A Primer on Neural Network Models for Natural Language Processing
  • Ian Goodfellow, Yoshua Bengio, and Aaron Courville. Deep Learning
  • Delip Rao and Brian McMahan. Natural Language Processing with PyTorch (requires Stanford login).
  • Michael A. Nielsen. Neural Networks and Deep Learning
  • Eugene Charniak. Introduction to Deep Learning

Conferences: ACL (Association for Computational Linguistics), EACL (European Association for Computational Linguistics), COLING (International Conference on Computational Linguistics), ICML (International Conference on Machine Learning), IJCNLP (International Joint Conference on Natural Language Processing), AAAI (American Association of Artificial Intelligence), ECAI (European Conference on AI), HLT/NAACl (Human language Technology/ North American Association for Computational Linguistics), ICON (International Conference on Natural Language Processing) etc.

CS4XXBig data Analytics2-0-2-6CS

Part 1: Introduction to Big Data:
Why Big Data and Where did it come from? Characteristics of Big Data- Volume, Variety, Velocity, Veracity, Valence, Value, Challenges and applications of Big Data

Part 2: Enabling Technologies for Big Data:
Introduction to Big Data Stack, Introduction to some Big Data distribution packages

Part 3: Big Data Computing Technology:
Overview of Apache Spark, HDFS, YARN, Introduction to MapReduce, MapReduce Programming Model with Spark, MapReduce Example: Word Count, Page Rank etc.

Part 4: Big Data Storage Technology:
CAP Theorem, Eventual Consistency, Consistency Trade-Offs, ACID and BASE, Introduction to Zookeeper and Paxos, Introduction to Cassandra, Cassandra Internals,Introduction to HBase, HBase Internals

Part 5: Big Data Analytics framework:
Introduction to Big Data Streaming Systems, Big Data Pipelines for Real-Time computing, Introduction to Spark Streaming, Kafka, Streaming Ecosystem

Part 6: Scalable Machine Learning for Big Data:
Overview of Big Data Machine Learning, Mahout Introduction, Big Data Machine Learning Algorithms in Mahout- kmeans, Naïve Bayes etc.

Part 7: Scalable Machine learning with Spark for Big Data Analytics:
Big Data Machine Learning Algorithms in Spark- Introduction to Spark MLlib, Introduction to Deep Learning for Big Data

Part 8: Large Scale Graph Processing for Big Data:
Introduction to Pregel, Introduction to Giraph, Introduction to Spark GraphX Laboratory Component: Big Data Analytics Practical sessions on the above topics.

Text Books:

  • Bart Baesens, Analytics in a Big Data World: The Essential Guide to Data Science and its Applications, Wiley, 2014

Reference Book:

  • Dirk Deroos et al., Hadoop for Dummies, Dreamtech Press, 2014.
  • Chuck Lam, Hadoop in Action, December, 2010 | 336 pages ISBN: 9781935182191
  • Mining of Massive Datasets. Leskovec, Rajaraman, Ullman, Cambridge University Press
  • Data Mining: Practical Machine learning tools and techniques, by I.H. Witten and E. Frank
  • Erik Brynjolfsson et al., The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies, W. W. Norton & Company, 2014
CS4XXElective-I3-0-0-6CS
CS4XXElective-II3-0-0-6CS
CS4XXCapstone Project-II0-0-3-3CS

The objective of this project would be to encourage and provide support to students for some innovative work. The work may focus on inventing a practical solution for a AI/DS or multidisciplinary problems. Depending on the nature of the work, it may be carried out in a group or individual mode.

Semester VIII

CS4XXBig data Security2-0-2-6CS

Data Security Overview, Basic Cryptography, symmetric key Encryption, Asymmetric key encryption, Hash function, User Authentication and Access Control, Database access control, Access control for Distributed system Cryptography for Big data Security, Homomorphic Encryption, Secure multiparty computation, Secure data access for big data Service, Integrating with cloud computing Security, Provable Data possession, Symmetric Secure Searchable Encryption, Asymmetric Secure Searchable Encryption, Privacy of out sourced data storage, Integrity of outsourced data storage and processing.

Text Books:

  • Database and Applications Security: Integrating Information Security and Data Management
  • Referred Journal/ Conference publication
XX4XXElective III3-0-0-6- -
CS4XXElective IV3-0-0-6-
CS4XXindividual Project3-0-0/6-6-

The students who work on a project are expected to work towards the goals and milestones set in AI&DS. At the end there would be demonstration of the solution and possible future work on the same problem. A dissertation outlining the entire problem, including a literature survey and the various results obtained along with their solutions is expected to be produced.

Proposed Electives

Database & Data Mining

Introduction to Computational Topology

Geometric and Topological Modelling for Scientists and Engineers

Mobile Robotics

Cloud Computing

Statistical signal processing

Estimation and Detection

information theory and coding

Introduction to Network Science

Cryptography

High Performance Computing

Social Text Mining,

AI in Healthcare

Conversational AI

Discrete Differential Geometry

Computational Geometry

Topological Data Analysis

Planning Algorithms,

A Mathematical Introduction to Robotics

Advanced Signal Processing for AI and DS

Edge AI

Statistical signal processing,

Estimation and Detection,

Applications of artificial intelligence in Chemistry

Graph Representation Learning,

Advanced Network Science,

Distributed Machine Learning

Deep Learning for NLP

Conversational Artificial Intelligence,

Machine Translation,

Information Retrieval and Mining,

Sentiment and Emotion Analysis

Advanced Operating Systems

Signal Processing and Machine Learning for Data Science

Applied Time Series Analysis

Probability and Random Process

Applied Time Series Analysis

B.Tech (CS) Courses

Course Structure  (I-IV Sem. for 2021 Onwards and V-VIII Sem. for 2019-20 Batches)  

 
SemesterCourse CodeCourse nameL-T-P-CreditOffering Department
Semester I CE111 Engineering Drawing 1-0-3-5 Civil
EE101 Electrical Sciences 3-1-0-8 Electrical
HS103 Communicative English for Engineers 2-0.5-1-6 Humanities and Social Science
MA101 Mathematics I 3-1-0-8 Mathematics
ME110 Workshop-I 0-0-3-3 Mechanical
PH103 Physics –I 3-1-0-8 Physics
PH 110 Physics Laboratory 0-0-3-3 Physics
Total credits: 41 
Semester II CB102 & CE102 Biology and Environmental Studies 3-0-0-6 CB & CE
CH103 Introductory Chemistry 3-1-0-8 Chemistry
CH110 Chemistry Laboratory 0-0-3-3 Chemistry
CS102 Programming and Data Structures 3-0-0-6 CS
CS112 Programming and Data Structures Laboratory 0-0-3-3 CS
EE103 Basic Electronics Laboratory 0-0-3-3 EE
MA102 Mathematics –II 3-1-0-8 Mathematics
ME102 Engineering Mechanics 3-1-0-8 ME
Total credits: 45 
Semester III MA2XX Mathematical III 3-1-0-8 Mathematics
HS2XX HSS Elective – I 3-0-0-6 Humanities and Social Science
CS2XX Algorithms 3-0-0-6 CS
CS22X Algorithms Laboratory 0-0-3-3 CS
CS2XX Discrete Mathematics 3-0-0-6 CS
CS2XX Digital Systems 2-0-2-6 CS
CS2XX Optimization techniques 3-0-0-6 CS
CS2XX Software Lab/Tools 0-0-3-3 CS
Total credits: 44
Semester IV HS2XX HSS Elective – II 3-0-0-6 Humanities and Social Science
MA2XX Open Elective I (Prob. Theory and Random Processes) 3-0-0-6 Mathematics
CS2XX Computer Architecture 3-0-0-6 CS
CS2XX Computer Architecture Lab 0-0-3-3 CS
CS2XX Theory of computation 3-0-0-6 CS
CS2XX Database 3-0-0-6 CS
CS2XX Database Lab 0-0-3-3 CS
Total credits: 36
Semester V XX3nn Open Elective 3-0-0-6  
CS303 Formal Language & Automata Theory 3-1-0-8 CS
CS321 Computer Architecture 3-0-0-6 CS
CS354 Database 3-0-0-6 CS
CS355 Database Lab 0-0-3-3 CS
CS322 Computer Architecture Lab 0-0-3-3 CS
Total credits: 32
Semester VI HS3nn HSS Elective 3-0-0-6 Humanities and Social Science
CS341 Operating System 3-0-0-6 CS
CS358 Computer Network 3-0-0-6 CS
CSXXX CS Elective 1 3-0-0-6 CS
CS359 Computer Network Lab 0-0-3-3 CS
CS399 Seminar 0-0-3-3 CS
CS342 Operating Systems Lab 0-1-3-5 CS
CS3XX Capstone Project 0-0-3-3 CS
Total credits: 35
Semester VII XX4nn Open Elective 3-0-0-6  
CSXXX CS Elective -2 3-0-3-9 CS
CSXXX CS Elective -3 3-0-0-6 CS
CSXXX CS Elective -4 3-0-0-6 CS
CS491 Project-I 0-0-6-6 CS
Total credits: 33
Semester VIII CSXXX CS Elective -5 3-0-0-6 CS
CSXXX CS Elective -6 3-0-0-6 CS
CSXXX CS Elective -7 3-0-0-6 CS
CS492 Project-II 0-0-12-12 CS
Total credits: 30
 Total credits for B.Tech CS: 296 

Semester I  

 
 

Engineering Drawing

CE111Engineering Drawing1-0-3-5Civil

Geometrical construction of simple plane figure:Bisecting the line, draw perpendicular, parallel line, bisect angle, trisect angle, construct equatorial triangle, square, polygon, inscribed circle.

Free hand sketching: prerequisites for freehand sketching, sketching of regular and irregular figures.

Drawing scales: Engineering scale, graphical scale, plane scale, diagonal scale, comparative scale, scale of chord.

Orthographic projection: Principle of projection, method of projection, orthographic projection, plane of projection, first angle of projection, third angle of projection, reference line.

Projection of points, lines and plane: A point is situated in the first quadrant, point is situated in the second quadrant, point is situated in the third quadrant, point is situated in the fourth quadrant, projection of line parallel to both the plane, line contained by one or both the plane, line perpendicular to one of the plane, line inclined to one plane and parallel to other, line inclined to both the plane, true length of line.

Missing views: Drawing of missing front view of a solid, missing top view of solids, missing side view of solids, Orthographic projection of simple solid: Introduction, types of solid, projection of solid when axis perpendicular to HP, axis perpendicular to VP, axis parallel to both HP and VP, axis inclined to both HP and VP.

Orthographic projection of simple solid: Introduction, types of solid, projection of solid when axis perpendicular to HP, axis perpendicular to VP, axis parallel to both HP and VP, axis inclined to both HP and VP.

Text and Reference Books:

  • B. Agrawal and CM Agrawal, Engineering Drawing, Tata McGraw-Hill Publishing Company Limited, 2008.
  • D. A. Jolhe, Engineering Drawing, Tata McGraw-Hill Publishing Company Limited, 2006.
  • K. Venugopal, Engineering Drawing and Graphics, 2nd ed., New Age International, 1994.

Electrical Sciences

EE101Electrical Sciences3-1-0-8Electrical

Circuit Analysis Techniques, Circuit elements, Simple RL and RC Circuits, Kirchhoff’s law, Nodal Analysis, Mesh Analysis, Linearity and Superposition, Source Transformations, Thevnin's and Norton's Theorems, Time Domain Response of RC, RL and RLC circuits, Sinusoidal Forcing Function, Phasor Relationship for R, L and C, Impedance and Admittance.

Semiconductor Diode, Zener Diode, Rectifier Circuits, Clipper, Clamper, Bipolar Junction Transistors, Transistor Biasing, Transistor Small Signal Analysis, Transistor Amplifier, Operational Amplifiers, Op-amp Equivalent Circuit, Practical Op-amp Circuits, DC Offset, Constant Gain Multiplier, Voltage Summing, Voltage Buffer, Controlled Sources, Instrumentation Circuits, Active Filters and Oscillators.

Number Systems, Logic Gates, Boolean Theorem, Algebraic Simplification, K-map, Combinatorial Circuits, Encoder, Decoder, Combinatorial Circuit Design, Introduction to Sequential Circuits.

Magnetic Circuits, Mutually Coupled Circuits, Transformers, Equivalent Circuit and Performance, Analysis of Three-Phase Circuits, Electromechanical Energy Conversion, Introduction to Rotating Machines.

Text and Reference Books:

  • C. K. Alexander and M. N. O. Sadiku, Fundamentals of Electric Circuits, 3rd Edition, McGraw-Hill, 2008.
  • W. H. Hayt and J. E. Kemmerly, Engineering Circuit Analysis, McGraw-Hill, 1993.
  • Donald A Neamen, Electronic Circuits; analysis and Design, 3rd Edition, Tata McGraw-Hill Publishing Company Limited.
  • Adel S. Sedra, Kenneth C. Smith, Microelectronic Circuits, 5th Edition, Oxford University Press, 2004.
  • R. L. Boylestad and L. Nashelsky, Electronic Devices and Circuit Theory, 6th Edition, PHI, 2001.
  • M. M. Mano, M. D. Ciletti, Digital Design, 4th Edition, Pearson Education, 2008.
  • Floyd and Jain, Digital Fundamentals, 8th Edition, Pearson.
  • A. E. Fitzgerald, C. Kingsley Jr. and S. D. Umans, Electric Machinery, 6th Edition, Tata McGraw-Hill, 2003.
  • D. P. Kothari and I. J. Nagrath, Electric Machines, 3rd Edition, McGraw-Hill, 2004.

Communicative English for Engineers

HS103Communicative English for Engineers2-0.5-1-6HSS

In today’s ‘global village’, there are many who believe that ‘Communication is like breathing and life would cease to continue without it’. This particular course on communication skills imbibes the same and therefore, it aims to equip the students with getting the basics right of communication and presentation skills for academic and professional purposes. It is designed to help the second language learners acquire fluency in both spoken and written English to communicate information with clarity, precision and confidence especially in the professional sphere. It will introduce learners not only to the basic concepts in communication but also focus on providing them a hands-on experience of the same. It is hoped that after commanding the skills required in spoken and written English, learners will be able to express themselves more effectively.

The course will have ten units and shall focus on the following topics:

Unit 1: Language and Communication
What is Communication
Nature, Style and Process of Communication
Communication Barriers
Objectives and Importance of Communication
Formal and Informal Communication
Verbal and Non-Verbal Communication

Unit 2: English Language Remedial Skills
Construction of Sentences
Subject-Verb Agreement
Tenses
Active and Passive Voice
Direct and Indirect Speech
Common Errors

Unit 3: Oral Skills
Public Speaking
Dealing with lack of confidence
Making an Effective Presentation
Telephone Etiquette
Understanding GD
Why conduct a GD?
How to gear up for a GD?
Different Phases of GD

Unit 4: Listening Skills
Meaning of Listening
Different Types of Listening
Barriers to Listening and Methods to overcome them
Various strategies to develop effective Listening
Semantic Markers

Unit 5: Reading Skills
What is Reading?
Types of Reading
Reading Comprehension

Unit 6: Writing Skills
Business Correspondence
Element and Style of Writing
Report Writing
Notice, Agenda and Minutes

Unit 7: Interview Techniques
How to prepare for an Interview
An Interview

Text and Reference Books:

  • V. S. Kumar, P.K. Dutt and G. Rajeevan, A Course in Listening and Speaking-I, Foundation books, 2007.
  • V.Sasikumar, P.KiranmaiDutt, Geetha Rajeevan, "A Course in Listening and Speaking-II', Foundation books, 2007.
  • Rizvi, Ashraf, Effective Technical Communication, Tata McGraw Hill, 2005.
  • Nitin Bhatnagar and MamtaBhatnagar, 'Communicative English for Engineers and Professionals, Pearson, 2010.

Mathematics I

MA101Mathematics I3-1-0-8Mathematics

Properties of real numbers. Sequences of real numbers, montone sequences, Cauchy sequences, divergent sequences. Series of real numbers, Cauchy’s criterion, tests for convergence. Limits of functions, continuous functions, uniform continuity, montoneand inverse functions. Differentiable functions, Rolle's theorem, mean value theorems and Taylor's theorem, power series. Riemann integration, fundamental theorem of integral calculus, improper integrals. Application to length, area, volume, surface area of revolution. Vector functions of one variable and their derivatives. Functions of several variables, partial derivatives, chain rule, gradient and directional derivative. Tangent planes and normals. Maxima, minima, saddle points, Lagrange multipliers, exact differentials. Repeated and multiple integrals with application to volume, surface area, moments of inertia. Change of variables. Vector fields, line and surface integrals. Green’s, Gauss’ and Stokes’ theorems and their applications.

Text Books:

  • G. B. Thomas and R. L. Finney, Calculus and Analytic Geometry, 6th Ed/9th Ed, Narosa/ Addison Wesley/ Pearson, 1985/ 1996.
  • T. M. Apostol, Calculus, Volume I, 2nd Ed, Wiley, 1967. T. M. Apostol, Calculus, Volume II, 2nd Ed, Wiley, 1969.

Reference Books:

  • R. G. Bartle and D. R. Sherbert, Introduction to Real Analysis, 5th Ed, Wiley, 1999.
  • J. Stewart, Calculus: Early Transcendentals, 5th Ed, Thomas Learning (Brooks/ Cole), Indian Reprint, 2003.

Workshop-I

ME110Workshop-I0-0-3-3Mechanical

Sheet Metal Working:

Sheet material: GI sheets, aluminium, tin plate, copper, brass etc.; Tools: steel rule, Vernier calipers, micrometer, sheet metal gauge, scriber, divider, punches, chisels, hammers, snips, pliers, stakes etc.; operations: scribing, bending, shearing, punching etc.; Product development: hexagonal box with cap, funnel etc.

Pattern Making and Foundry Practice:

Pattern material: wood, cast iron, brass, aluminium, waxes etc.; Types of patterns: split, single piece, match plate etc.; Tools: cope, drag, core, core prints, shovel, riddle, rammer, trowel, slick, lifter, sprue pin, bellow, mallet, vent rod, furnace etc. Moulding sands: green sand, dry sand, loam sand, facing sand etc., Sand casting: Sand preparation, mould making, melting, pouring, and cleaning. Joining: Classifications of joining processes; Introduction to Arc welding processes; power source; electrodes; edge preparation by using tools bench vice, chisels, flat file, square file, half round file, round file, knife edge file, scrapers, hacksaws, try squares; cleaning of job, Job: lap and butt joints using manual arc welding.

Machining Centre:

Introduction to different machine tools; Working principle of lathe, milling, drilling etc.; Setting and preparation of job using lathe and milling; Performing different operations namely, straight turning, taper turning, knurling, thread cutting etc.; Introduction to dividing head, indexing, performing operation in milling using indexing mechanism.

CNC Centre:

Introduction to CNC machines; Fundamentals of CNC programming using G and M code; setting and operations of job using CNC lathe and milling, tool reference, work reference, tool offset, tool radius compensation.

Text and Reference Books:

  • H. Choudhury, H. Choudhary and N. Roy, Elements of Workshop Technology, vol. I,Mediapromoters and Publishers Pvt. Ltd., 2007.
  • W. A. J. Chapman, Workshop Technology, Part -1, 1st South Asian Edition, Viva Book Pvt Ltd., 1998.
  • P.N. Rao, Manufacturing Technology, Vol.1, 3rd Ed., Tata McGraw Hill PublishingCompany, 2009.
  • B.S. Pabla, M.Adithan, CNC machines,New Age International, 2012.
  • G. B. Thomas and R. L. Finney, Calculus and Analytic Geometry, 6th Ed/9th Ed, Narosa/Addison Wesley/Pearson, 1985/1996.
  • T. M. Apostol, Calculus, Volume I, 2nd Ed, Wiley, T. M. Apostol, Calculus, Volume II, 2nd Ed, Wiley, 1969/1967.

Physics-I

PH103Physics-I3–1–0–8PH

Orthogonal coordinate systems and frames of reference, conservative and non-conservative forces, work-energy theorem, potential energy and concept of equilibrium; Rotation about fixed axis, translational-rotational motion, vector nature of angular velocity, rigid body rotation and its applications, Euler's equations; Gyroscopic motion and its application; Accelerated frame of reference, centrifugal and Coriolis forces.

Harmonic oscillator, damped and forced oscillations, resonance, coupled oscillations, small oscillation, normal modes, longitudinal and transverse waves, wave equation, plane waves, phase velocity, superposition wave packets and group velocity, two- and three-dimensional waves.

Failure of classical concepts, Black body radiation, photo-electric effect, Compton effect, Davison and Germer's experiment, Frank-Hertz experiment, Bohr's theory, Sommerfeld's model, correspondence principle, Planck hypothesis, De Broglie's hypothesis, Hilbert space, observables, Dirac notation, principle of superposition, wave packets, phase and group velocities, probability & continuity equation, eigenvalues and Eigen functions, orthonormality, expectation values, uncertainty principle, postulates of Quantum Mechanics, Schrodinger equation & its applications to 1D potentials, field quantization, periodic potential wells: Kronig Penny model and origin of band gap.

Textbooks:

  • D. Kleppner and R. J. Kolenkow, An introduction to Mechanics, Tata McGraw-Hill, New Delhi, 2000.
  • David Morin, Introduction to Classical Mechanics, Cambridge University Press, NY, 2007.
  • Frank S. Crawford, Berkeley Physics Course Vol 3: Waves and Oscillations, McGraw Hill, 1966.
  • Eyvind H. Wichmann, Berkeley Physics Course Vol 4: Quantum physics, McGraw Hill, 1971.

Reference Books:

  • R. P. Feynman, R. B. Leighton and M. Sands, The Feynman Lecture in Physics, Vol I, Narosa Publishing House, New Delhi, 2009.
  • R. P. Feynman, R. B. Leighton and M. Sands, The Feynman Lecture in Physics, Vol III, Narosa Publishing House, New Delhi, 2009.
  • R. Eisberg and R. Resnick, Quantum Physics of atoms, molecules, solids, nuclei and particles, John Wiuley and Sons (Asia) Pvt. Ltd., Singapore, 2002.
  • A. J. Dekker, Solid State Physics, Macmillan Pub. India Ltd., New Delhi, 2009.
  • David J. Griffith, Introduction to Quantum Mechanics, Pearson Education Ltd, New Delhi, 2009.
  • B.H. Bransden& C.J. Joachain, Quantum Mechanics, Pearson Education Ltd, New Delhi, 2008.

Physics Laboratory

PH110Physics Laboratory0-0-3-3PH

The list of experiments is as follows:

  • Instructions to Students
  • Introduction to Error Analysis

Ex 1 Decay of Current in A Capacitive Circuit

Ex 2 Q-Factor of an LCR Circuit

Ex 3 Study of Hall Effect

Ex 4 Speed of Sound in Air

Ex 5 ‘g’ by A Compound Pendulum

Ex 6 Speed of Light in Glass

Ex 7 Determination of e/m

Ex 8 Interference of Light: Newton’s Ring

Ex 9 Surface Tension of Water by Method of Capillary Ascent

Ex 10 Determination of Plank’s constant by Photoelectric Effect

Second Semester  

 

Biology and Environment Studies

CB102&CE102Biology and Environment Studies3-0-0-6CB & CE

Module 1 - Biology: 1. Cell – Structure and logic of optimization; 2. Blood – The following tissue – Basis and rationale; 3. Organs – Structure, function, interactions, failure; 4. Molecular basis of disorders – example: Diabetes; 5. Modern techniques of evaluations and corrections; 6. Open discussions – Feedback from students

Module 2 – Environmental Science / Studies: 1.Ecology and Sustainable Development – Ecosystems, Natural cycles, Biodiversity, Man and environment; 2. Water Resources – Hydrologic cycle and its components, Groundwater and surface water, Water quality; 3. Environmental Sanitation: Conventional and ecological sanitation; 4. Environmental Pollution and Control – Air, Water, Soil, Noise Pollution, Solid and Hazardous Waste, Biomedical Waste, E-waste: Sources, effect, treatment and control; 5. Environmental Legislations and Standards; 6.Current Environmental Issues: Greenhouse gases and global warming, Acid rain, Ozone layer depletion, Climate change

Text Books:

  • Any basic Biology Book of CBSE Curriculum at +2 Level/ E-text Books
  • Davis, M.L. and Masten,S.J., Principles of Environmental Engineering and Science,2nd Edition, McGraw-Hill, 2013.
  • Kaushik, A. and Kaushik, C.P., Perspectives in Environmental Studies, 4thEdition, New Age International, 2014.

Reference Books:

  • Botkin,D.B. and Keller,E.A., Environmental Science,8th Edition, Wiley, 2012.
  • Cunningham, W.P. and Cunningham, M.A., Environmental Science: A Global Concern, 13thEdition, McGraw-Hill, 2015
CH103Introductory Chemistry3-1-0-8Chemistry

PHYSICAL CHMEISTRY

Thermodynamics: The fundamental definition and concept, the zeroth and first law. Work, heat, energy and enthalpies. Second law: entropy, free energy and chemical potential. Change of Phase. Third law. Chemical equilibrium, Chemical kinetics: The rate of reaction, elementary reaction and chain reaction.

Electrochemistry: Conductance of solutions, equivalent and molar conductivities and its variation with concentration. Kohlrausch’s law-ionic mobilities, Transference number of ions. activities, application of Debye-Huckel theory. The Walden’s rule. Debye-Huckel-Onsager treatment. Electrochemical cells, Nernst equation. Application of EMF measurements. Liquid junction potential, commercial cells – the primary and secondary cells. Fuel cells.

INORGANIC CHEMISTRY

Coordination chemistry: ligand, nomenclature, isomerism, stereochemistry, valence bond, crystal field and molecular orbital theories. Bioinorganic chemistry: Trace elements in biology, heme and non-heme oxygen carriers, haemoglobin and myoglobin; organometallic chemistry.

ORGANIC CHEMISTRY
Stereo and regio-chemistry of organic compounds, conformers. Bioorganic chemistry: amino acids, peptides, proteins, enzymes, carbohydrates, nucleic acids and lipids. Modern techniques in structural elucidation of compounds (UV – Vis, IR, NMR). Solid phase synthesis and combinatorial chemistry. Green chemical processes.

Textbooks:

  • P. W. Atkins, Physical Chemistry, ELBS, 5th Ed, 1994.
  • J. O'M. Bockris and A. K. N. Reddy, Modern Electrochemistry, Vol. 1 and 2, Kluwer Academic, 2000.
  • K. L. Kapoor, A Textbook of Physical Chemistry, Macmillan India, 2nd Ed, 1986.
  • F. A. Cotton and G. Wilkinson, Advanced Inorganic Chemistry, Wiley Eastern Ltd, New Delhi, 3rd Ed, 1972 (reprint in 1998).
  • D. J. Shriver, P. W. Atkins and C. H. Langford, Inorganic Chemistry, ELBS, 2nd Ed, 1994.
  • S. H. Pine, Organic Chemistry, McGraw Hill, 5th Ed, 1987

Reference Books:

  • Levine, Physical Chemistry, McGraw Hill, 4th Ed, 1995.
  • J. E. Huheey, E. A. Keiter and R. L. Keiter, Inorganic Chemistry: Principle, structure and reactivity, Harper Collins, 4th Ed, 1993.
  • L. G. Wade Jr., Organic Chemistry, Prentice Hall, 1987
CH110Chemistry Laboratory0-0-3-3Chemistry

Estimation of metal ion: Determination of total hardness of water by EDTA titration. Experiments based on chromatography: Identification of a mixture containing two organic compounds by TLC. Experiments based on pH metry.: Determination of dissociation constant of weak acids by pH meter. Experiments based on conductivity measurement: Determination of amount of HCl by conductometric titration with NaOH. Synthesis and characterization of inorganic complexes: e.g. Mn(acac)3, Fe(acac)3, cis-bis(glycinato)copper (II) monohydrate and their characterization by m. p. IR etc. Synthesis and characterization of organic compounds: e.g. Dibenzylideneacetone. Kinetics: Acid catalyzed hydrolysis of methyl acetate. Verification of Beer-Lamberts law and determination of amount of iron present in a supplied solution. Experiments based on electro gravimetry and electroplating. Experiments based on magnetometry.

CS102Programming and Data Structures3-0-0-6CS

Introduction to digital computers; introduction to programming- variables, assignments; expressions; input/output; conditionals and branching; iteration; functions; recursion; arrays; introduction to pointers; structures; introduction to data-procedure encapsulation; dynamic allocation; linked structures; introduction to data structures stacks, queues and trees; time and space requirements.

References:

  • B. W. Kernighan and D. Ritchie, The C Programming Language, Prentice Hall of India (2nd Edition).
  • A. Kelley and I. Pohl, A Book on C, Pearson Education (4th Edition).
  • P.J. Deitel and H.M. Deitel , C How To Program, Pearson Education (7th Edition).
CS112Programing and Data Structures Laboratory0-0-3-3CS

Introduction to Unix commands; Introduction to program development tools- vi editor, GNU compiler, testing and debugging, etc.; Implementation of programs in C language.

EE103Basic Electronics Laboratory0-0-3-3EE

Experiments using diodes and bipolar junction transistor (BJT): design and analysis of half -wave and full-wave rectifiers, clipping circuits and Zener regulators, BJT characteristics and BJT amplifiers; experiments using operational amplifiers (op- amps): summing amplifier, comparator, precision rectifier, astable and mono stable multi vibrators and oscillators; experiments using logic gates: combinational circuits such as staircase switch, majority detector, equality detector, multiplexer and demultiplexer; experiments using flip-flops: sequential circuits such as non- overlapping pulse generator, ripple counter, synchronous counter, pulse counter and numerical display.

Reference Books:

  • A. P. Malvino, Electronic Principles. New Delhi: Tata McGraw-Hill, 1993.
  • R. A. Gayakwad, Op-Amps and Linear Integrated Circuits. New Delhi: Prentice Hall of India, 2002.
  • R.J. Tocci: Digital Systems; PHI, 6e, 2001.
MA102Mathematics-II3-1-0-8MA

Linear Algebra: Vector spaces (over the field of real and complex numbers). Systems of linear equations and their solutions. Matrices, determinants, rank and inverse. Linear transformations. Range space and rank, null space and nullity. Eigenvalues and eigenvectors. Similarity transformations. Diagonalization of Hermitian matrices. Bilinear and quadratic forms.

Ordinary Differential Equations: First order ordinary differential equations, exactness and integrating factors. Variation of parameters. Picard's iteration. Ordinary linear differential equations of n-th order, solutions of homogeneous and non-homogeneous equations. Operator method. Method of undetermined coefficients and variation of parameters.

Power series methods for solutions of ordinary differential equations. Legendre equation and Legendre polynomials, Bessel equation and Bessel functions of first and second kind. Systems of ordinary differential equations, phase plane, critical point, stability.

Textbooks:

  • K. Hoffman and R. Kunze, Linear Algebra, Prentice Hall, 1996.
  • T. M. Apostol, Calculus, Volume II, 2nd Ed, Wiley, 1969.
  • S. L. Ross, Differential Equations, 3rd Ed, Wiley, 1984.
  • E. A. Coddington, An Introduction to Ordinary Differential Equations, Prentice Hall, 1995.
  • W.E. Boyce and R.C. DiPrima, Elementary Differential Equations and Boundary Value Problems, 7th Ed, Wiley, 2001.

Reference Books:

  • E. Kreyszig, Advanced Engineering Mathematics, 9th Edition, Wiley, 2005.
ME102Engineering Mechanics3-1-0-8MA
  1. Rigid body statics: Equivalent force system. Equations of equilibrium, Freebody diagram, Reaction, Static indeterminacy.
  2. Structures: 2D truss, Method of joints, Method of section. Beam, Frame, types ofloading and supports, axial force, Bending moment, Shear force and Torque Diagrams for a member:
  3. Friction: Dry friction (static and kinetic), wedge friction, disk friction (thrust bearing), belt friction, square threaded screw, journal bearings, Wheel friction, Rolling resistance.
  4. Centroid and Moment of Inertia
  5. Virtual work and Energy method: Virtual Displacement, principle of virtual work, mechanical efficiency, work of a force/couple (springs etc.), Potential Energy and equilibrium, stability.
  6. Introduction to stress and strain: Definition of Stress, Normal and shear Stress. Relation between stress and strain, Cauchy formula.
  7. Stress in an axially loaded member,
  8. Stresses due to pure bending,
  9. Complementary shear stress,
  10. Stresses due to torsion in axi-symmetric sections:
  11. Two-dimension state of stress, Mohr’s circle representation, Principal stresses

Text and Reference books:

  • I. H. Shames, Engineering Mechanics: Statics and dynamics, 4th Ed, PHI, 2002.
  • F. P. Beer and E. R. Johnston, Vector Mechanics for Engineers, Vol I - Statics, 3rd Ed, TataMcGraw Hill, 2000.
  • J. L. Meriam and L. G. Kraige, Engineering Mechanics, Vol I - Statics, 5th Ed, John Wiley, 2002.
  • E.P. Popov, Engineering Mechanics of Solids, 2nd Ed, PHI, 1998.
  • F. P. Beer and E. R. Johnston, J.T. Dewolf, and D.F. Mazurek, Mechanics of Materials, 6th Ed, McGraw Hill Education (India) Pvt. Ltd., 2012.

Third Semester  

 
MA201Mathematical-III3–1–0–8MA

Complex Analysis: Complex numbers, geometric representation, powers and roots of complex numbers. Functions of a complex variable: Limit, Continuity, Differentiability, Analytic functions, Cauchy-Riemann equations, Laplace equation, Harmonic functions, Harmonic conjugates. Elementary Analytic functions (polynomials, exponential function, trigonometric functions), Complex logarithm function, Branches and Branch cuts of multiple valued functions. Complex integration, Cauchy's integral theorem, Cauchy's integral formula. Liouville’s Theorem and Maximum-Modulus theorem, Power series and convergence, Taylor series and Laurent series. Zeros, Singularities and its classifications, Residues, Rouches theorem (without proof), Argument principle (without proof), Residue theorem and its applications to evaluating real integrals and improper integrals. Conformal mappings, Mobius transformation, Schwarz-Christoffel transformation.

Fourier series: Fourier Integral, Fourier series of 2p periodic functions, Fourier series of odd and even functions, Half-range series, Convergence of Fourier series, Gibb’s phenomenon, Differentiation and Integration of Fourier series, Complex form of Fourier series.

Fourier Transformation: Fourier Integral Theorem, Fourier Transforms, Properties of Fourier Transform, Convolution and its physical interpretation, Statement of Fubini’s theorem, Convolution theorems, Inversion theorem

Partial Differential Equations: Introduction to PDEs, basic concepts, Linear and quasi-linear first order PDE, Second order PDE and classification of second order semi-linear PDE, Canonical form. Cauchy problems. D’ Alembert’s formula and Duhamel’s principle for one dimensional wave equation, Laplace and Poisson equations, Maximum principle with application, Fourier method for IBV problem for wave and heat equation, rectangular region. Fourier method for Laplace equation in three dimensions.

Text Books:

  • R. V. Churchill and J. W. Brown, Complex Variables and Applications, 5th Edition, McGraw-Hill, 1990.
  • K. Sankara Rao, Introduction to Partial Differential Equations, 2nd Edition, 2005.

Reference Books:

  • J. H. Mathews and R. W. Howell, Complex Analysis for Mathematics and Engineering, 3rd Edition, Narosa, 1998.
  • I. N. Sneddon, Elements of Partial Differential Equations, McGraw-Hill, 1957. E. Kreyszig, Advanced Engineering Mathematics, 9th Edition, Wiley, 2005.
HS2XXHSS Elective – I3–0–0–6HSS
CS2XXAlgorithms3–0–0–6CS

Asymptotic notations, introduction to complexity (time/space) analysis of algorithms. Basic introduction to algorithmic paradigms like divide and conquer, recursion, greedy, dynamic programming, etc. Searching: binary search trees, balanced binary search trees, AVL trees and red-black trees, B-trees, hashing. Priority queues, heaps, Interval trees. Sorting: quick sort, heap sort, merge sort, radix sort, bucket sort, counting sort, etc. and their analysis. Graph Algorithms: BFS, DFS, connected components, topological sort, minimum spanning trees, shortest paths, network flow. Reducibility between problems and NP-completeness: discussion of different NP-complete problems.

Books

  • M. A. Weiss, Data Structures and Problem-Solving Using Java, 2nd Ed, Addison-Wesley, 2002.
  • T. H. Cormen, C. E. Leiserson, R. L. Rivest and C. Stein, Introduction to Algorithms, MIT Press, 2001.
  • B. W. Kernighan and D. Ritchie, The C Programming Language, 2nd Ed, Prentice
  • Hall of India, 1988.
  • A. Aho, J. E. Hopcroft and J. D. Ullman, The Design and Analysis of Computer
  • Algorithms, Addison-Wesley, 1974.
  • S. Sahni, Data Structures, Algorithms and Applications in C++, McGraw-Hill, 2001.
  • M. T. Goodrich and R. Tamassia, Algorithm Design: Foundations, Analysis and Internet
  • Examples, John Wiley & Sons, 2001.
CS22XAlgorithms Laboratory0–0–3–3CS

The laboratory component will emphasize two areas: Implementation of algorithms covered in class: This will involve running the algorithms under varying input sets and measuring running times, use of different data structures for the same algorithm (wherever applicable) to see its effect on time and space, comparison of different algorithms for the same problem etc. Design of Algorithms: This will involve design and implementation of algorithms for problems not covered in class but related to topics covered in class. The exact set of algorithms to design and implement is to be decided by the instructor. In addition, there will be at least one significantly large design project involving some real-world application. An efficient design of the project should require the use of multiple data structures and a combination of different algorithms/techniques. The lab work can be carried out using any programming language.

CS2XXDiscrete Mathematics3–0–0–6CS

Propositional logic: Syntax, semantics, valid, satisfiable and unsatisfiable formulas, encoding and examining the validity of some logical arguments; Recurrences, summations, generating functions, asymptotic; Sets, relations and functions: Operations on sets, relations and functions, binary relations, partial ordering relations, equivalence relations, principles of mathematical induction, Finite and infinite sets, countable and uncountable sets, Cantor’s diagonal argument and the power set theorem; Introduction to counting: Basic counting techniques - inclusion and exclusion, pigeon-hole principle, permutation, combination, generating function; Algebraic structures and morphisms: semigroups, groups, subgroups, homomorphism, rings, integral domains, fields; Introduction to graphs: paths, connectivity, subgraphs, isomorphic and homeomorphic graphs, trees, complete graphs, bipartite graphs, matchings, colourability, planarity, digraphs;

Text Books:

  • J. P. Tremblay and R. P. Manohar, Discrete Mathematics with Applications to Computer Science, Tata McGraw-Hill, 1999.
  • C. L. Liu, Elements of Discrete Mathematics, 2nd Ed, Tata McGraw-Hill, 2000.
  • R. L. Graham, D. E. Knuth, and O. Patashnik, Concrete Mathematics, 2nd Ed, Addison-Wesley,1994.
  • N. Deo, Graph Theory with Applications to Engineering and Computer Science, Prentice Hall of India, 1974.
  • S. Lipschutz and M. L. Lipson, Schaums Outline of Theory and Problems of Discrete Mathematics, 2ndEd, Tata McGraw-Hill, 1999
CS2XXDigital Systems2–0–2–6CS

Number Systems, Boolean algebra, logic gates, minimization of completely and incompletely specified switching functions, Karnaugh map and Quine-McCluskey method, multiple output minimization, twolevel and multi-level logic circuit synthesis. Clocks, flip-flops, latches, counters and shift registers, finite state machine model, synthesis of synchronous sequential circuits, minimization and state assignment, Programmable logic devices: memory design. Data path control path partition-based design.

Experiments: Combinational logic circuits: Design and implementation of combinational circuits such as ALU and 7-segment LED display driver; Sequential Circuits: Design of sequence generators and detectors, counters, design of ASMs such as, traffic light controllers, lift controllers, etc. Digital design project: The students design and implement a final digital project of their choice.

References:

  • Z. Kohavi, Switching and Finite Automata Theory, 2nd Ed, Tata McGraw-Hill, 1995.
  • M. M. Mano, Digital Design, 3rd Ed, Pearson Education Asia, 2002.
  • S. Brown and Z. Vranesic, Fundamentals of Digital Logic - With Verilog Design, Tata McGraw-Hill, 2002.
  • S. Brown and Z. Vranesic, Fundamentals of Digital Logic - With VHDL Design, Tata McGraw-Hill, 2002 .
  • J. P Uyemura, A First Course in Digital System Design - An Integrated Approach, Vikas Publishing House, 2001.
CS2XXOptimization techniques3–0–0–6CS

Linear programming: Introduction and Problem formulation, Concept from Geometry, Geo-metrical aspects of LPP, Graphical solutions, Linear programming in standard form, Simplex, Big M and Two-Phase Methods, Revised simplex method, Special cases of LPP.

Duality theory: Dual simplex method, Sensitivity analysis of LP problem, Transportation, Assignment and travelling salesman problem.

Integer programming problems: Branch and bound method, Gomory cutting plane method for all integer and for mixed integer LPP.

Theory of games: saddle point, linear programming formulation of matrix games, two-person zero-sum games with and without saddle-points, pure and mixed strategies, graphical method of solution of a game, solution of a game by simplex method. Computational complexity of the Simplex algorithm, Karmarkar's algorithm for LPP. Acquaintance to softwares like TORA and MATLAB.

Text Books:

  • Hamdy A. Taha, Operations Research: An Introduction, Eighth edition, PHI, New Delhi (2007).
  • S. Chandra, Jayadeva, Aparna Mehra, Numerical Optimization with Applications, Narosa Publishing House (2009).
  • A. Ravindran, D.T. Phillips, J.J. Solberg, Operation Research, John Wiley and Sons, New York (2005).
  • M. S. Bazaraa, J. J. Jarvis and H. D. Sherali, Linear Programming and Network Flows, 3rd Edition, Wiley (2004).

Reference Books:

  • D. G. Luenberger, Linear and Nonlinear Programming, 2nd Edition, Kluwer, (2003).
  • S. A. Zenios (editor), Financial Optimization, Cambridge University Press (2002).
  • F. S. Hiller, G. J. Lieberman, Introduction to Operations Research, Eighth edition, McGraw Hill (2006).
CS2XXSoftware Lab/Tools0–0–3-3CS

Bash shell programming – basic concepts, expressions, decision making selections, repetition, special parameters - positional parameters, shift, argument validation, script examples.

Android Basics: Getting started with Android development, project folder structure, simple programming, running project, generating build/APK of the app from Android Studio

First application: Creating Android Project, Android Virtual Device Creation, set up debugging environment, Workspace set up for development, launching emulator, debugging on mobile devices. Basic UI design: Basics about Views, Layouts, Drawable Resources, input controls, Input Events etc. Understand the app idea and design user interface/wireframes of mobile app

Set up the mobile app development environment.

Fourth Semester  

 
HS2XXHSS Elective-II3-0-0-6HSS
MA2XXOpen Elective I (Prob. Theory and Random Processes)3-0-0-6MA

Algebra of sets, probability spaces, random variables, cumulative distribution functions, mathematical expectations, conditional probability and expectation, moments and inequalities, special discrete and continuous probability distributions, function of a random variable, random vectors and their distributions, convolutions, joint, marginal and conditional distributions, product moments, independence of random variables, bivariate distributions and properties, order statistics and their distributions, sampling distributions, Central Limit Theorem, strong law of large numbers, sequence of random variables, modes of convergence, distributions of the sample mean and the sample variance for a normal population, chi-square, t and F distributions, method of moments and maximum likelihood estimation, concepts of unbiasedness, criteria for choosing estimators, consistency and efficiency of estimates, confidence intervals, pivotal quantities, confidence intervals for proportions, simple and composite hypothesis, null and alternative hypotheses, types of error, level and size of tests, the most powerful test and Neyman - Pearson Fundamental Lemma, tests for one- and two-sample problems for normal populations, tests for proportions, likelihood ratio tests, chi-sqaure test for goodness of fit. discrete and continuous stochastic processes, markov chains, transition probability matrix, state spaces, classification of states, stationary distributions, ergodicity, poisson process, birth and death process. Introduction to reliability analysis: Application of Bayes theorem in real life problem; Reliability analysis of simple syste serial, parallel and combined systems; First order uncertainty and reliability analysis (FORM), First order second mom (FOSM) and Advanced FOSM methods; Applications of risk and reliability analysis in engineering systems.

Text / Reference Books:

  • Scheaffer, R. L., Mulekar, M. S. and McClave, J. T., (2011): Probability and statistics for Engineers, Fifth Edition, Broo Cole, Cengage Learning.
  • Ang, A. H-S., and Tang, W. H., (2006): Probability Concepts in Engineering, Volumes 1. John Wiley and Sons.
  • Halder, A and Mahadevan, S., (2000): Probability, Reliability and Statistical Methods in Engineering Design, John Wiley Sons.
  • Rao, S.S., (1992): Reliability-Based Design, McGraw Hill, Inc.
  • Harr, M.E., (1987): Reliability-Based Design in Civil Engineering. McGraw Hill, Inc.
  • Ang, A. H-S, and Tang, W. H., (1975): Probability Concepts in Engineering Planning and Design, Volumes 2. John Wiley and Sons
  • Benjamin, J., and Cornell. A., (1963): Probability, Statistics, and Decision for Civil Engineers. McGraw Hill.
CS2XXComputer Architecture3-0-0-6CS

CPU - registers, instruction execution cycle, RTL interpretation of instructions, addressing modes, instruction set. Case study - instruction sets of some common CPUs; Assembly language programming for some processor; Data representation: signed number representation, fixed and floating point representations, character representation. Computer arithmetic - integer addition and subtraction, ripple carry adder, carry look-ahead adder, etc. multiplication – shift-and-add, Booth multiplier, carry save multiplier, etc. Division - non-restoring and restoring techniques, floating point arithmetic; CPU control unit design: hardwired and micro-programmed design approaches, Case study - design of a simple hypothetical CPU; Pipelining: Basic concepts of pipelining, throughput and speedup, pipeline hazards; Memory organization: Memory interleaving, concept of hierarchical memory organization, cache memory, cache size vs block size, mapping functions, replacement algorithms, write policy; Peripheral devices and their characteristics: Input-output subsystems, I/O transfers - program controlled, interrupt driven and DMA, privileged and non-privileged instructions, software interrupts and exceptions. Programs and processes - role of interrupts in process state transitions.

CSXXComputer Architecture Lab0-0-3-3CS

Familiarization with assembly language programming; Synthesis/design of simple data paths and controllers, processor design using HDL like verilog/vhdl; Interfacing - DAC, ADC, keyboard-display modules, etc. Development kits as well as Microprocessors/PCs may be used for the laboratory, along with design/simulation tools as and when necessary.

CS2XXTheory of computation3-0-0-6CS

Regular Languages: Finite Automata-Deterministic and Nondeterministic, regular operations, Regular Expressions, Equivalence of DFA, NFA and Res, Nonregular Languages and pumping lemma

Context-Free Languages: Context-Free Grammars, Chomsky Normal Form, Pushdown Automata, Non Context-Free Languages and pumping lemma, Deterministic Context-Free Languages

Turing Machines: Definition of TM and its variants, Decidability, Reducibility.

Complexity Theory: Time complexity and Space Complexity.

Text Books:

  • Introduction to the Theory of Computation, by Michael Sipser.
  • Computational Complexity, by Christos H. Papadimitriou, Addison-Wesley publishers.
  • Computational Complexity: A Modern Approach, by Sanjeev Arora and Boaz Barak.
CS2XXDatabase3-0-0-6CS

Database system architecture: Data Abstraction, Data Independence, Data Definition and Data Manipulation Languages; Data models: Entity-relationship, network, relational and object oriented data models, integrity constraints and data manipulation operations; Relational query languages: Relational algebra, tuple and domain relational calculus, SQL and QBE; Relational database design: Domain and data dependency, Armstrong’s axioms, normal forms, dependency preservation, lossless design; Query processing and optimization: Evaluation of relational algebra expressions, query equivalence, join strategies, query optimization algorithms; Storage strategies: Indices, B-trees, hashing; Transaction processing: Recovery and concurrency control, locking and timestamp based schedulers, multi-version and optimistic Concurrency Control schemes; Recent Trends: XML Data, XML Schema, JSON and “NoSQL Systems, etc.

Books:

  • Abraham Silberschatz, Henry Korth, and S. Sudarshan, Database System Concepts, McGraw-Hill.
  • Raghu Ramakrishnan, Database Management Systems, WCB/McGraw-Hill.
  • Bipin Desai, An Introduction to Database Systems, Galgotia.
  • J. D. Ullman, Principles of Database Systems, Galgotia.
  • R. Elmasri and S. Navathe, Fundamentals of Database Systems, Addison-Wesley.
  • Serge Abiteboul, Richard Hull and Victor Vianu, Foundations of Databases. Addison-Wesley
CS2XXDatabase Lab0-0-3-3CS

Database schema design, database creation, SQL programming and report generation using a commercial RDBMS like ORACLE/SYBASE/DB2/SQL-Server/INFORMIX. Students are to be exposed to front end development tools, ODBC and CORBA calls from application Programs, internet based access to databases and database administration.

Fifth Semester  

 
XX3nnOpen Elective3-0-0-6--
CS303Formal Language & Automata Theory3-1-0-8CS

Introduction: Alphabet, languages and grammars, productions and derivation, Chomsky hierarchy of lan- guages. Regular languages and finite automata: Regular expressions and languages, deterministic finite automata (DFA) and equivalence with regular expressions, nondeterministic finite automata (NFA) and equivalence with DFA, regular grammars and equivalence with finite automata, properties of regular languages, pumping lemma for regular languages, minimization of finite automata. Context-free languages and pushdown automata: Context-free grammars (CFG) and languages (CFL), Chomsky and Greibach normal forms, nondeterministic pushdown automata (PDA) and equivalence with CFG, parse trees, ambiguity in CFG, pumping lemma for context-free languages, deterministic pushdown automata, closure properties of CFLs. Context-sensitive languages: Context-sensitive grammars (CSG) and languages, linear bounded automata and equivalence with CSG. Turing machines: The basic model for Turing machines (TM), Turing- recognizable (recursively enumerable) and Turing-decidable (recursive) languages and their closure properties, variants of Turing machines, nondeterministic TMs and equivalence with deterministic TMs, unrestricted grammars and equivalence with Turing machines, TMs as enumera- tors. Undecidability: Church-Turing thesis, universal Turing machine, the universal and diagonalization languages, reduction between languages and Rice’s theorem, undecidable problems about languages.

References:

  1. J. E. Hopcroft, R. Motwani and J. D. Ullman, Introduction to Automata Theory, Languages and Computation, Pearson Education India (3rd edition).
  2. K. L. P. Mishra, N. Chandrasekaran, Theory of Computer Science: Automata, Languages and Computation, PHI Learning Pvt. Ltd. (3rd edition).
  3. D. I. A. Cohen, Introduction to Computer Theory, John Wiley & Sons, 1997.
  4. J. C. Martin, Introduction to Languages and the Theory of Computation, Tata McGraw-Hill (3rd Ed.).
  5. H. R. Lewis and C. H. Papadimitriou, Elements of the Theory of Computation, Prentice
CS321Computer Architecture3-0-0-6CS

Basic functional blocks of a computer: CPU, memory, input-output subsystems, control unit. Instruction set architecture of a CPU - registers, instruction execution cycle, RTL interpretation of instructions, addressing modes, instruction set. Case study - instruction sets of some common CPUs; Assembly language programming for some processor; Data representation: signed number representation, fixed and floating point representations, character representation. Computer arithmetic - integer addition and subtraction, ripple carry adder, carry look-ahead adder, etc. multiplication – shift-and-add, Booth multiplier, carry save multiplier, etc. Division - non-restoring and restoring techniques, floating point arithmetic; CPU control unit design: hardwired and micro- programmed design approaches, Case study - design of a simple hypothetical CPU; Pipelining: Basic concepts of pipelining, throughput and speedup, pipeline hazards; Memory organization: Memory interleaving, concept of hierarchical memory organization, cache memory, cache size vs block size, mapping functions, replacement algorithms, write policy; Peripheral devices and their characteristics: Input-output subsystems, I/O transfers - program controlled, interrupt driven and DMA, privileged and non-privileged instructions, software interrupts and exceptions. Programs and processes - role of interrupts in process state transitions.

References:

  1. David A. Patterson, John L. Hennessy, Computer Organization and Design, Fourth Edition: The Hardware/Software Interface, Morgan Kaufmann; 4 edition, 2011.
  2. A. Tenenbaum, Structured Computer Organization, 4th Ed, Prentice-Hall of India, 1999.
  3. W. Stallings, Computer Organization and Architecture: Designing for Performance, 6th Ed, Prentice Hall, 2005.
  4. J. Hennessy and D. Patterson, Computer Architecture A Quantitative Approach, 3rd Ed, Morgan Kaufmann, 2002.
CS322Computer Architecture Lab0-0-3-3CS

Familiarization with assembly language programming; Synthesis/design of simple data paths and controllers, processor design using HDL like verilog/vhdl; Interfacing - DAC, ADC, keyboard-display modules, etc. Development kits as well as Microprocessors/PCs may be used for the laboratory, along with design/simulation tools as and when necessary.

CS354Database3-0-0-6CS

Database system architecture: Data Abstraction, Data Independence, Data Definition and Data Manipulation Languages; Data models: Entity-relationship, network, relational and object oriented data models, integrity constraints and data manipulation operations; Relational query languages: Relational algebra, tuple and domain relational calculus, SQL and QBE; Relational database design: Domain and data dependency, Armstrongs axioms, normal forms, dependency preservation, lossless design; Query processing and optimization: Evaluation of relational algebra expressions, query equivalence, join strategies, query optimization algorithms; Storage strategies: Indices, B-trees, hashing; Transaction processing: Recovery and concurrency control, locking and timestamp based schedulers, multiversion and optimistic Concurrency Control schemes; Recent Trends: XML Data, XML Schema, JSON and “NoSQL Systems, etc,.

References:

  1. Abraham Silberschatz, Henry Korth, and S. Sudarshan, Database System Concepts, McGraw-Hill.
  2. Raghu Ramakrishnan, Database Management Systems, WCB/McGraw-Hill.
  3. Bipin Desai, An Introduction to Database Systems, Galgotia.
  4. J. D. Ullman, Principles of Database Systems, Galgotia.
  5. R. Elmasri and S. Navathe, Fundamentals of Database Systems, Addison-Wesley.
  6. Serge Abiteboul, Richard Hull and Victor Vianu, Foundations of Databases. Addison- Wesley
CS355Database Laboratory0-0-3-3CS

Database schema design, database creation, SQL programming and report generation using a commercial RDBMS like ORACLE/SYBASE/DB2/SQL-Server/INFORMIX. Students are to be exposed to front end development tools, ODBC and CORBA calls from application Programs, internet based access to databases and database administration.

CS322Computer Architecture Lab0-0-3-3CS

Sixth Semester  

 
HS3nnHSS Elective3-0-0-6HSS
CS341Operating System3-0-0-6CS

Process Management: process; thread; scheduling. Concurrency: mutual exclusion; synchronization; semaphores; monitors; Deadlocks: characterization; prevention; avoidance; detection. Memory Management: allocation; hardware sup- port; paging; segmentation. Virtual Memory: demand paging; replacement; allocation; thrashing. File Systems and Implementation. Secondary Storage: disk structure; disk scheduling; disk management. (Linux will be used as a running example, while examples will drawn also from Windows NT/7/8.); Advanced Topics: Distributed Systems. Security. Real-Time Systems.

References:

  1. Silberschatz, P. B. Galvin and G. Gagne, Operating System Concepts, 9th Ed, John Wiley & Sons, 2010.
  2. A. S. Tenenbaum, Modern Operating Systems, 2nd Ed, Prentice Hall of India, 2001.
  3. H. M. Deitel, P. J. Deitel and D. R. Choffness, Operating Systems, 3rd Ed, Prentice Hall, 2004.
  4. W. Stallings, Operating Systems: Internal and Design Principles, 5th Ed, Prentice Hall, 2005.
  5. M. J. Bach, The Design of the UNIX Operating System, Prentice Hall of India, 1994.
  6. M. K. McKusick et al, The Design and Implementation of the 4.4 BSD Operating System, Addison Wesley, 1996.
CS342Operating System Laboratory0-1-3-5CS

Programming assignments to build different parts of an OS kernel.

CS358Computer Network3-0-0-6CS

Evolution of computer networks; Physical Layer: Theoretical basis for data communication, transmission media and impairments, switching systems Medium Access Control Sublayer: Channel allocation Problem, multiple access protocols, Ethernet Data link layer: Framing, HDLC, PPP, sliding window protocols, error detection and correction Network Layer: Internet addressing, IP, ARP, ICMP, CIDR, routing algorithms (RIP, OSPF, BGP); Transport Layer: UDP, TCP, flow control, congestion control; Introduction to quality of service; Application Layer: DNS, Web, email, authentication, encryption.

References:

  1. Peterson & Davie, Computer Networks, A Systems Approach: 5th Edition
  2. William Stallings Data and Computer Communication, Prentice Hall of India.
  3. Behrouz A. Forouzan, Data Communication and Networking, McGraw-Hill.
  4. Andrew S. Tanenbaum, Computer Networks, Prentice Hall.
  5. Douglas Comer, Internetworking with TCP/IP, Volume 1, Prentice Hall of India.
  6. W. Richard Stevens, TCP/IP Illustrated, Volume 1, Addison-Wesley.
CS359Computer Network Lab0-0-3-3CS

Simulation experiments for protocol performance, configuring, testing and measuring network devices and parameters/policies; network management experiments; Exercises in network programming.

Seventh Semester  

 
CS491Project-I0-0-6-6CS

The project can span the course Project-II. Hence it is expected that the problem specification and the milestones to be achieved in solving the problem are clearly specified.

Eighth Semester  

 
CS492Project-II0-0-12-12CS

The students who work on a project are expected to work towards the goals and milestones set in course Project-I. At the end there would be demonstration of the solution and possible future work on the same problem. A dissertation outlining the entire problem, including a literature survey and the various results obtained along with their solutions is expected to be produced.

M.Tech Courses

Core Courses:  

 
CS541Foundations of Computer Systems3-0-0-6Pre-requisites: Nil

Review of concepts of computer architecture: Study of an existing CPU: architecture, instruction set and the addressing modes, assembly language programming. Control unit Design: instruction interpretation, hardwired and micro-programmed methods of design. Pipelining and parallel processing, RISC and CISC paradigms, I/O Transfer techniques: programmed, interrupt-driven and DMA; Memory organization: hierarchical memory systems, cache memories, cache coherence, virtual memory.

Review of concepts of operating systems: Processes, threads, Unix fork-exec model, Unix signals, Interprocess communication, scheduling, memory management.

Review of concepts of computer networks: link layer protocols, local area networks (Ethernet and variants), interconnecting networks with IP, routing, transport layer protocols. Advanced concepts of distributed networked systems: Virtualization, distributed file systems, mass storage systems, recovery and fault tolerance, content networking including multimedia delivery.

Texts:

  • A. Silberschatz, P. B. Galvin and G. Gagne, Operating System Concepts, 7th Ed, John Wiley and Sons, 2004.
  • J. Kurose and K. W. Ross, Computer Networking: A Top down approach, 3rd Ed, Pearson India, 2004. M. Singhal and N. Shivratri, Advanced Concepts in Operating Systems, McGraw Hill, 1994. A. S. Tanenbaum and Van Steen, Distributed Systems: Principles and Paradigms, Prentice Hall India, 2007.
CS511:Foundations of Theoretical Computer Science3-0-0-6Pre-requisites: Nil

Discrete Structures Sets, Relations and Functions; Proof Techniques, Algebraic Structures, Morphisms, Posets, Lattices and Boolean Algebras.

Logic Propositional calculus and Predicate Calculus, Satisfiabiliy and validity, Notions of soundness and completeness. Automata and Languages -- Finite automata and regular expressions, pushdown automata and context-free grammars, pumping lemmas and closure properties of regular and context-free languages, non-context-free languages.

Computability theory Church-Turing thesis, Hilbert's problem, decidability, halting problem, reducibility; Complexity theory: time and space complexity, Classes P, NP, NP-complete, PSPACE, and PSPACE-complete

Texts:

  • M. Sipser, Introduction to the Theory of Computation, Thomson, 2004. 2. H. R. Lewis, C. H. Papadimitriou, Elements of the Theory of Computation, PHI, 1981.

References:

  • J. E. Hop croft, J. D. Ullman, Introduction to Automata Theory, Languages and Computation,Narosa,1979.
  • S. Arora, B. Barak, Computational Complexity: A Modern Approach, Cambridge UniversityPress,2009.
  • C. H. Papadimitriou, Computational Complexity, Addison-Wesley Publishing Company, 1994.
  • D.C.Kozen, Theory of Computation, Springer, 2006.
  • D. S. Garey, G. Johnson, Computers and Intractability: A Guide to the Theory of NP-Completeness, Freeman, New York, 1979.
CS 512:Data Structure and Algorithms3-0-0-6Pre-requisites: Nil

Problem Solving using Computers - Abstraction - Abstract data types; Data Representation; Elementary data types; Basic concepts of data Structures; Mathematical preliminaries - big-Oh notation; efficiency of algorithms; notion of time and space complexity; performance measures for data structures.

ADT array - Computations on arrays -sorting and searching algorithms. ADT Stack, Queue, list - array, linked list, cursor based implementations of linear structures.

ADT Tree - tree representation, traversal of trees;

ADT Binary tree - binary trees, threaded binary trees, application of binary trees -Huffman coding; application of threaded binary trees - differentiation;

Search Tree - Binary search tree; balanced binary search trees - AVL tree; Applications of Search Trees - TRIE; 2-3 tree, 2-3-4 tree; concept of B-Tree.

ADT Dictionary - array based and tree based implementations; hashing - definition and application - LZW encoding. ADT Priority Queue - Heaps; heap-based implementations; applications of heaps - sorting; Graphs - shortest path, minimum spanning tree, DFS, BFS - an application of DFS and BFS.

Algorithm Design Paradigms - greedy, divide and conquer, dynamic Programming, backtracking.

References:

  • Mark Allen Weiss, "Data Structures and Algorithms in C++", Addison Wesley, 2003.
  • Adam Drozdek, "Data Structures and Algorithms in C++", Brooks and Cole, 2001.
  • Aho, Hopcroft and Ullmann, "Data structures and Algorithm", Addison Welsey, 1984.
MA501:Probability, Statistics and Stochastic Processes3-0-0-6Pre-requisites: Nil

Probability : algebra of sets, monotone class, sigma fields, Borel sigma fields, set function, product spaces, measurable transformations, probability measure, notions of probability space and some consequences, Borel-Cantelli Lemma Discrete, continuous and mixed type probability spaces, cumulative distribution functions, probability mass (density) functions, mathematical expectations, general concepts of conditional probability and expectation, conditional expectation given a sigma field, properties of conditional expectation, moments, moment and probability generating functions, moment inequalities: Markov, Chebyshev-Bienayme, Lyapunov.

Special Probability Distributions : Discrete and continuous uniform, binomial, beta, Cauchy, Negative Binomial, Hypergeometric, Gamma, Normal, Lognormal, Weibull, Pareto distributions, generalized distributions, Approximation properties of discrete distributions, Beta-Binomial and Poisson-Gamma relationship. Function of a random variable, random vectors and their distributions, convolutions, Joint, marginal and conditional distributions, product moments, correlation, independence of random variables, bivariate distributions and properties, order statistics and their distributions, further properties.

Sampling Distributions: The Central Limit Theorem, Demoivre theorem, uniform convergence in CLT, characteristics functions, continuity theorems, strong law of large numbers, Sequence of random variables, modes of convergence and some results, Slutsky theorem, distributions of the sample mean and the sample variance for a normal population, Chi - Square, t and F distributions and their distributional properties Point and Interval Estimation : The method of moments and the method of maximum likelihood estimation, large sample properties, concepts of unbiasedness, criteria for choosing estimators, consistency and efficiency of estimates, confidence intervals for the parameters of common distributions, pivotal quantities, confidence intervals for proportions (one and two samples problems) .

Testing of Hypotheses : simple and composite hypothesis, Null and alternative hypotheses, critical and acceptance regions, two types of error, level and size of test, error probabilities of a test, the most powerful test and Neyman - Pearson Fundamental Lemma, tests for one sample and two sample problems for normal populations, tests for one sample and two sample proportions, Likelihood ratio tests, Chisqaure test for goodness of fit.

Stochastic Processes: illustrations of stochastic processes, stochastic matrices, Markov chains: finite and countably infinite state spaces, Classification of states, strong markov property, stationary distributions, time reversible markov chains, Branching processes, ergodic and nonergodic markov chains, recurrent and transient random walk, General Markov processes in discrete and continuous state spaces. Poisson process: homogeneous and nonhomogeneous,
pure birth process, birth and death process, regenerative processes, notions of queuing models

References:

  • Rohatgi, V.K., and Saleh, A.K.Md. Ehsanes (2009). An introduction to probability and statistics. Second Edition, Wiley India.
  • Introduction to the Theory of Statistics; Alexander M. Mood, Franklin A. Graybill, Duane C. Boes, Tata McGraw Hill.
  • Milton, J.S. and Arnold, J.C. (2009) Introduction to Probability and Statistics, Fourth Edition, Tata Mcgraw-Hill.
  • Ross, S.M.(2008) Introduction to Probability Models, Ninth edition, Academis Press.
  • Statistical Inference (2007), G. Casella and R.L. Berger, Duxbury Advanced Series .
  • Ash, Robert B. (2009), Probability & Measure Theory, Academic Press.

Elective Courses (Elective I –III)  

 
CS561:Artificial Intelligence3-0-0-6Pre-requisites: Nil

Introduction, Problem Solving: Uninformed search, Informed search, local Search, Online search; Knowledge and Reasoning: Building a Knowledge Base, Semantic Nets, Frames, First order logic, Inference in First Order Logic; Probabilistic Reasoning Systems: Bayes’ Nets; Learning: Learning from examples and analogy, Naive Bayes, Computational Learning Theory, Explanation Based Learning, Neural Networks; Evolutionary Optimization: Genetic algorithms, Multi objective optimization, Differential Evolution, Particle Swarm Optimization; Introduction to NLP;

Introduction to Fuzzy sets.

References:

  • S. Russel and P. Norvig. Artificial Intelligence: A Modern Approach (Second edition), Pearson
  • E. Charniak, Introduction to Artificial Intelligence, Addison Wesley, 1985.
  • P. H. Winston, Artificial Intelligence, Addison Wesley, 1993.
  • E. Rich and K. Knight, Artificial Intelligence, Addison Wesley, 1990.
  • R.Honavar and E. Uhr, Artificial Intelligence and Neural Networks, Academic Press, 1992.
  • F. Hayes Roth, Building Expert Systems, Addison Wesley, 1983.
  • P. R. Cohen, The Handbook of Artificial Intelligence, Vol.1,2 and 3, Kaufman Inc.,1982.
  • B. K. P. Horn, Robot Vision, MIT Press, 1985. J. Carbonell, Machine Learning paradigms and Methods, MIT Press, 1990.

Journals:- Artificial Intelligence, AI Magazine, IEEE Expert, Machine Learning, Computer Vision Image Processing and Graphics, IEEE Transactions on Neural Networks.

CS542:Software Testing3-0-0-6Pre-requisites: Nil

Testing Background, The Realities of Software Testing, Verification and Validation, Testing Fundamentals, Examining the Specification, Examining the Code, Configuration Testing, Compatibility Testing, Usability Testing, Special Testing Techniques; Test Management, Test Automation and Testing Tools; Recent Trends in Software Testing;

Books:

  • Software Testing by Ron Patton, Sams Publishing
  • Lessons Learned in Software Testing by Kaner, Bach and Pettichord
CS543:Distributed Systems3-0-0-6Pre-requisites:Nil

Basic concepts. Models of computation: shared memory and message passing systems, synchronous and asynchronous systems. Logical time and event ordering. Global state and snapshot algorithms, mutual exclusion, clock synchronization, leader election, deadlock detection, termination detection, spanning tree construction. Programming models: remote procedure calls, distributed shared memory. Fault tolerance and recovery: basic concepts, fault models, agreement problems and its applications, commit protocols, voting protocols, checkpointing and recovery, reliable communication. Security and Authentication: basic concepts, Kerberos. Resource sharing and load balancing. Special topics: distributed objects, distributed databases, directory services, web services.

References:

  • Mukesh Singhal and Niranjan Shivaratri, Advanced Concepts in Operating Systems, McGraw-Hill.
  • Nancy Lynch, Distributed Algorithms, Morgan Kaufmann.
  • Andrew S. Tanenbaum, Distributed Operating Systems, ACM Press.
  • Jie Wu, Distributed Systems, CRC Press.
  • Hagit Attiya, Jennifer Welch, Distributed Computing: Fundamentals, Simulations and Advanced Topics, McGraw-Hill.
  • Sape Mullender (ed.), Distributed Systems, Addison-Wesley.
MA511:Large Scale Scientific Computation3-0-0-6Pre-requisites:Nil

Introduction to sparse matrices, Storage Schemes, Permutations and Reorderings, , Sparse Direct Solution Methods. Iterative methos and Preconditioning Convergence Krylov Subspaces, Arnoldi’s Method, GMRES, Symmetric Lanczos Algorithm, conjugate Gradient Algorithm, Convergence Analysis, Block Krylov Methods, Preconditioned Conjugate Gradient, Preconditioned GMRES, Jacobi, SOR, and SSOR Preconditioners, ILU Factorization Preconditioners, Block Preconditioners, Types of Partitionings, Techniques, Direct Solution and the Schur Complement, Schur Complement Approaches, Full Matrix Methods, Graph Partitioning: Geometric Approach, Spectral Techniques. Newton’s method and some of its variations, Newton method in several dimension, continuation methods, conjugate direction method and Davidon-Fletcher-Powell Algorithms, Introduction to Non- linear Multigrid with applications. HPC kernels (BLAS, multicore and GPU computing)

Texts / References:

  • O. Axelsson, Iterative Solution Methods Cambridge Univ. Press, 1994.
  • W. Hackbusch, Multigrid Methods and Applications. Springer-Verlag, 1985.
  • J.M. Ortega and W.C. Rheinboldt, Iterative Solution of Nonlinear Equations in Several Variables. Academic Press, NY, 1970.
  • C.W. Ueberrhuber, Numerical Computation : Methods, Software and Analysis. Springer-Verlag, Berlin, 1997.
  • P. Wesseling, An Introduction to Multigrid Methods. John Wiley & Sons, 1992.
  • Yousef Saad, Iterative Methods for Sparse Linear Systems, SIAM 2003.
CS528:CAD for VLSI3-0-0-6Pre-requisites:Nil

Introduction: VLSI design flow, challenges.

Verilog/VHDL: introduction and use in synthesis, modeling combinational and sequential logic, writing test benches. Logic synthesis: two-level and multilevel gate-level optimization tools, state assignment of finite state machines. Basic concepts of high-level synthesis: partitioning, scheduling, allocation and binding. Technology mapping. Testability issues: fault modeling and simulation, test generation, design for testability, built-in self-test. Testing SoC's. Basic concepts of verification. Physical design automation. Review of MOS/CMOS fabrication technology. VLSI design styles: full-custom, standard-cell, gate-array and FPGA.

Physical design auto-mation algorithms: floor-planning, placement, routing, compaction, design rule check, power and delay estimation, clock and power routing, Special considerations for analog and mixed-signal designs.

References::

  • D. D. Gajski, N. D. Dutt, A.C.-H. Wu and S.Y.-L. Lin, High-Level Synthesis: Introduction to Chip and System Design”Springer, 1st edition, 1992.
  • Giovanni De Michelli, “Synthesis and Optimization of Digital Circuits” McGraw-Hill Higher Education, 1994
  • N. A. Sherwani, “Algorithms for VLSI Physical Design Automation”, Bsp Books Pvt. Ltd., 3rd edition, 2005.
CS 544:Introduction to Network Science3-0-0-6Pre-requisites: Probability & Statistics

Introduction and background knowledge of complex networks; Network analysis metrics like paths, components, degree distribution, clustering, degree correlations, centrality etc., social network analysis methods; Introduction to network analysis tools like iGraph and Pajek; Properties of networks like scale-free, small world; Network evolution models like random networks, preferential attachment models and its variants, Watts & Strogatz model; Community detection methods and real world application of community detection; Dynamics on networks like percolation,
spreading, synchronization and real world applications.

Texts:

  • M.E.J. Newman, Networks - An introduction , Oxford Univ Press, 2010.

References:

  • R. Cohen and S. Havlin, Complex Networks - Structure, Robustness and Function , Cambridge Univ Press, 2010.
  • Barrat, M. Barthelemy and A. Vespignani, Dynamical Processes on Complex Networks, Cambridge Univ Press, 2008.
  • D. Easley and J. Kleinberg, Networks, Crowds and Markets , Cambridge Univ Press, 2010.

Elective Courses (Elective IV –VI)  

 
CS 548:Wireless Networks3-0-0-6Pre-requisites:Nil

Wireless technologies: Antennas and radio propagation. Signal encoding and modulation techniques. Spread spectrum. Coding and error control.

Wireless Networking: Cellular wireless networks and systems principles. Mobile IP and Wireless Access Protocol. Multiple access techniques.

Wireless LANs: Wireless LAN technology. Wireless standard (IEEE 802.11 etc.). Bluetooth. Ad-hoc Networks. Architectures and routing protocols for hybrid wireless networks, Issues and challenges in wireless sensor networks:

Texts:

  • W. Stallings, "Wireless Communications and Networks", Pearson Education, 2nd Ed.

References:

  • T S Rappaport, "Wireless Communications: Principles & Practice", Second Edition, Pearson Education, 2002.
  • J Schiller, "Mobile Communications", Addison Wesley, 2000.
  • V K Garg, "IS-95 CDMA and CDMA2000", Prentice Hall PTR, 2000.
  • Murthy, "Adhoc Wireless Networks: Architectures and Protocols", Pearson, 2004.
  • Research papers.
CS 549:Computer And Network Security3-0-0-6Pre-requisites: Nil

Overview: vulnerabilities, risk assessment, incidents.

Cryptography: Classical Cryptography, Symmetric Cryptography, Public Key (Asymmetric cryptography), Modern Cryptography(RSA, ECC), Hash Functions, Digital Signature.

Authentication and Key Management : Entity authentication, Key exchange, Key management, Kerberos Networking. Security: Security at application layer (PGP, S/MIME), Security at Transport Layer (SSL and TLS), Security at Network Layer (IPSEC)

System Security: Unix Security, Vulnerabilities and Counter Measures (Viruses, worms, Trojan horses, backdoors, buffer overflows, RPC), Exploits (Buffer overflow, Port Scanning etc). Firewalls, VPN etc, Secure (commerce) Transaction over a network.

Current network security Issues: Texts :

  • W. Stallings, Cryptography and Network Security: Principles and Practice, 5th Ed,Prentice Hall

References:

  • B. Schneier, Applied Cryptography, 2nd Ed, John Wiley & Sons, Inc., 1996.
  • A. Menezes, P. van Oorshot and S. Vanstone, Handbook of Applied Cryptogrphy, CRC Press, 1997.
  • B.A. Foruzan and D. Mukhopadhyay Cryptography and Network Security, second edn.Tata Mchrawhill
  • Related publications in Journals/Conferences.
CS 508:Formal Methods for Analysis and Verification3-0-0-6Pre-requisites:Nil

Introduction to formal methods; Analysis Vs. Verification; Correctness and soundness theorem; Formal semantics: operational, denotational, axiomatic; Specification Languages; Various formal methods and their application to verification and analysis: Model Checking, Abstract Interpretation, Shadow semantics, Hoare logic, Theorem Proving.

References:

  • Flemming Nielson, Hanne R. Nielson, Chris Hankin. Principles of Program Analysis, Springer, 1999.
  • Edmund M. Clarke, Orna Grumberg, Doron A. Peled. Model Checking, The MIT Press, 1999.
  • Glynn Winskel. The formal semantics of programming languages: an introduction, The MIT Press, 1993.
  • Annabelle McIver, Carroll Morgan. Abstraction, Refinement and Proof for Probabilistic Systems, Springer, 2005 edition.
  • Recent Research Papers relevant to the course.
CS502:Pattern Recognition3-0-0-6Pre-requisites:Nil

Syllabus : Introduction to Pattern Recognition: Learning paradigms, Supervised and unsupervised learning; Bayesian decision theory: Minimum error rate classifier.

Parameter estimation: Maximum likelihood and Bayesian Estimation; Hidden Markov models; Nonparametric techniques: Nearest neighbor rules, Parzen windows; Decision trees: Axis-parallel, Oblique, Impurity measures; Feature selection: Forward, backward search; Component analysis and discriminate functions: Principal component analysis, Fisher linear discriminate, Perceptron, Support vector machines; Generalization ability of learning methods: Bias and variance, Regularization; Bootstrapping, Boosting, Bagging; Unsupervised learning and clustering: k-Means methods.

Texts:

  • R. O. Duda, P. E. Hart and D. G. Stork, Pattern classification, John Wiley & Sons, 2002.
  • S. Theodoridis and K. Koutroumbas , Pattern Recognition, 4th Edition, Academic Press, 2008.

References:

  • C. M. Bishop, Neural Networks for Pattern Recognition, Oxford University Press, 1995.
  • V. N. Vapnik, The Nature of Statistical Learning Theory, Springer, 2000.
  • N. Cristianini and J. Shawe-Taylor, An Introduction to Support Vector Machines, Cambridge University Press, 2000.
  • Selected Research Papers.
CS743:Advanced Topics in Database Systems3-0-0-6Pre-requisites:Nil

Database Computation Models: Page and Object Models

Correctness in databases: Serializability - review of the basic theory, multiversion serializability, semantic serializability, relative atomicity, relative serializability, etc.

Concurrency control methods: Two phase locking, timestamp and optimistic methods, tree locking. Correctness in Software Transactional Memory (STM): Opacity, Virtual Worlds Consistency, Abort Shielded Consistency.

Page model crash and recovery: Expanded Schedules, Page-Model Correctness Criteria, Sufficient Syntactic Conditions, Further Relationships Among Criteria, Extending Page-Model CC Algorithms, Redo, undo algorithms. Object model crash and recovery: Unified concurrency control and recovery, compensating transactions, Algorithm for 2-Layered Systems, Algorithm for General Executions.

Special Database Systems: Object based, Semi-structured, Active, Deductive, Temporal, Spatial, Multimedia. Database Security: Access Control Models MAC, DAC, RBAC.

Datawarehousing: Multidimensional data model, OLAP, Data Warehouse Architecture This course will draw materials mainly from the books given below. However, there are many research papers that will help understand the course contents. These will be provided on time to time basis.

Suggested Text Books:

  • G. Weikum and G. Vossen – “Transactional Information Systems: Theory, Algorithms and the Practice of Concurrency Control and Recovery”, (Morgan Kaufmann), 2002
  • A. Silberschatz, H. F. Korth and S. Sudarsan – “Database System Concepts”, (McGraw Hill), 2011
  • R. Elamsri, S. B. Navathe – “Fundamentals of Database Systems”, (Pearson Education), 2011
  • R.Kimball – Data Warehouse Toolkit (J.Wiley & Sons), 2nd Edition 2002

Reference Books:

  • P. A. Bernstein, V. Hadzilacos and N. Goodman - Concurrency Control and Recovery in Database Systems, (Addison Wesley), 1987
  • P. A. Bernstein and E. Newcomer – Principles of Transaction Processing, (Morgan Kaufmann), 1997
  • A. Elmagarmid (Ed.)- Database Transaction Models for Advanced Applications, (Morgan Kaufmann), 1992.
  • J. Pieprzyk, T. Hardjono and J. Seberry – Fundamentals of Computer Security. (Springer), 2009 .

Lab Courses  

 
CS558:Computer Systems Lab – 13-0-0-6Pre-requisites:Nil

Basics of OS programming: process creation and synchronization, shared memory and semaphore, shell programming.

Socket programming, database creation and update, building large client server applications. Basics of compiler writing using lex and yacc .

CS513:Computer Systems Lab – 23-0-0-6Pre-requisites:Nil

Object-oriented programming concepts and implementation of abstract data types. Implementation of graph algorithms. Linear programming with applications.

Ph.D. Courses

Distributed Systems and Alogorithms  

 
CS701Distributed Systems and Alogorithms3-0-0-6

Basic concepts. Models of computation: shared memory and message passing systems, synchronous and asynchronous systems. Logical time and event ordering. Global state and snapshot algorithms, mutual exclusion, clock synchronization, leader election, deadlock detection, termination detection, spanning tree construction. Programming models: remote procedure calls, distributed shared memory. Fault tolerance and recovery: basic concepts, fault models, agreement problems and its applications, commit protocols, voting protocols, checkpointing and recovery, reliable communication. Security and Authentication: basic concepts, Kerberos. Resource sharing and load balancing. Special topics: distributed objects, distributed databases, directory services, web services.

Texts:

  • B. W. Kernighan and D. Ritchie, The C Programming Language, 2nd Ed, Prentice Hall of India, 1988.
  • N. Lynch, Distributed Algorithms, Elsevier India Private Limited, (2009)
  • Hagit Attiya, Jennifer Welch, Distributed Computing: Fundamentals, Simulations and Advanced Topics, Wiley, (2006)

References:

  • S.Ghosh, Distributed Systems: An Algorithmic Approach, Chapman & Hall, (2006)
  • A. Kshemkalyani, M. Singhal, Distributed Computing: Principles, Algorithms, and Systems, Cambridge University Press, (2008)
  • Gerard Tel, Introduction to Distributed Algorithms, 2nd edition, Cambridge University Press, (2004)
  • Technical papers from major distributed systems journals and conferences

Topics in Computer Networks  

 
CS741Topics in Computer Networks3-0-0-6

Overview of computer networks, the Internet and OSI model Responsibilities of the Data Link Layer, Network Layer and Transport Layer; Local Area Networks – Ethernet, Token Ring etc. Scheduling algorithms and MAC layer protocols (Link Layer) Routing protocols - BGP, RIP, OSPF, AODV etc. (Network Layer). Congestion control Algorithms (Transport Layer). Peer-to-peer and client-server programming using sockets in TCP and UDP. Quality of Service (QoS) Provisioning Network Security

Texts:

  • A. S. Tanenbaum, Computer Networks, 4th Ed, Prentice Hall, (2003)

References:

  • B. A. Forouzan, Data Communications and Networking, 3rd Ed, McGraw Hill, (2004)
  • W. Stallings, Data and Computer Communications, 7th Ed, Prentice Hall of India, (2004)
  • J. F.Kurose and K. W. Ross,Computer networking: A Top-down Approach Featuring the Internet, 3rd Ed, Addison-Wesley, (2005)
  • W. Stevens and G. Wright, TCP/IP Illustrated, Volumes 1-3, (2002)
  • Technical papers from major networking journals and conferences

Cryptography and Network Security  

 
CS742Cryptography and Network Security3-0-0-6

Symmetric key encryption algorithm, Key distribution, Stream Ciphers, Pseudo Random Numbers, Public Key Cryptography, Hashes and Message Digests, Digital Signatures, Certificates, User Authentication. System authentication, IPSec, Virtual Private Networks Secure Socket layer, transport layer security.

Texts:

  • W. Stalling: Cryptography and Network security Principles and Practices, 4th Edition PHI, (2006)
  • D. R. Stinson: Cryptography theory and practices, 2nd Edition, CRC Press,(2006)

References:

  • Technical papers from major reputed journals and conferences and internet resources

Advance in Alogorithms  

 
CS702Advance in Alogorithms3-0-0-6

Algorithmic paradigms: Dynamic Programming, Greedy, Branch-and-bound; Asymptotic complexity, Amortized analysis; Graph Algorithms: Shortest paths, Flow networks; NP-completeness; Approximation algorithms; Randomized algorithms; Linear programming; Special topics: Geometric algorithms (range searching, convex hulls, segment intersections, closest pairs), Numerical algorithms (integer, matrix and polynomial multiplication, FFT, extended Euclid's algorithm, modular exponentiation, primality testing, cryptographic computations), Internet algorithms (text pattern matching, tries, information retrieval, data compression, Web caching).

References:

  • T. H. Cormen, C. L. Leiserson, R. L. Rivest, and C. Stein, Introduction to Algorithms, 2nd edition, Prentice-hall Of India Pvt.. Ltd, (2007)
  • J. Kleinberg and E. Tardos, Algorithm Design, Addison-Wesley, (2008)
  • Rajeev Motwani and Prabhakar Raghavan, Randomized Algorithms, Cambridge University Press, (1995)
  • Vijay Vazirani, Approximation Algorithms, Springer, (2004)
  • Soumen Chakrabarti, Mining the Web: Discovering Knowledge from Hypertext Data, Elsevier India Private Limited, (2005)

References:

  • Technical papers from major reputed journals in the area of algorithms design.