CS551: Introduction to Deep Learning

This course will provide a basic understanding of deep learning and how to solve problems from varied domains. Open source tools will be used to demonstrate different applications.

  • Monday - 0800-0900
  • Tuesday - 0800-0900
  • Wednesday - 1700-1800

Brief introduction of big data problem. Overview of linear algebra, probability, numerical computation. Basics of Machine learning/Feature engineering. Neural network. Tutorial for Tools. 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, SGD, Unsupervised learning - Deep Belief Network, Deep Boltzmann Machine, Factor analysis, Autoencoders. Regularization. Optimization for training deep model. Advanced topics - Convolutional Neural Network, Recurrent Neural Network/ Sequence modeling, LSTM, Reinforcement learning. Practical applications – Vision, speech, NLP, etc.

  • Ian Goodfellow, Yoshua Bengio and Aaron Courville, “Deep Learning”, Book in preparation for MIT Press, 2016. (available online)
  • Jerome H. Friedman, Robert Tibshirani, and Trevor Hastie, “The elements of statistical learning”, Springer Series in Statistics, 2009.
  • Charu C Aggarwal, “Neural Networks and Deep Learning”, Springer.