CS 726 - Advanced Machine Learning

Course content
  • Modelling, generation, and prediction of multiple inter-dependent variables.
  • The topics covered will span probabilistic graphical models (directed and undirected),
  • Inference methods like junction trees.
  • Belief propagation, and other approximate methods,
  • MCMC sampling methods like Gibbs and Langevin,
  • Generative models like variational auto-encoders,
  • GANs, Deep Gaussian processes,
  • Neural architectures for structured predictions,
  • Neural density estimation methods, causality, and other recent topics in machine learning.
  • Probabilistic Graphical Models: Principles and Techniques, by Daphne Koller and Nir Friedman, MIT Press, 2009
  • Deep Learning by Ian Goodfellow, Yoshua Bengio and Aaron Courville, MIT Press, 2016
Pre-requisite :
  • A formal introductory ML course like CS 725 or CS 337 or CS 419 or DS 303 is required. Online ML courses do not qualify as pre-requisites.
  • The course assumes basic knowledge of probability, statistics, and linear algebra.
  • Chapters 2 and 3 of the Deep-learning book are a good place to refresh the necessary required background.
  • Also, the course assumes basic background in machine learning, for example as covered in Chapter 5 of the Deep-learning book and deep learning, for example, as covered in Chapter 6 of the same book. Further, we will assume that students are familiar with CNNs, RNNs, and sequence to sequence learning with attention.
Total credits : 6
Type : Theory
Duration :
Name(s) of other Academic units to whom the course may be relevant :