Syllabus (Core): M.Tech

Advanced Topics in Machine Learning

Code: EE 525 | L-T-P-C : 3-0-0

Kernel Methods: Review of SVM, Classification and Regression using SVM, Properties of Kernels, Non-Mercer Kernels, Kernel Selection, Multiple Kernel Learning, Kernel PCA; Probabilistic Graphical Models: Bayesian networks, Undirected models, Bayesian learning, structure learning, Inference on graphical models, exponential families; Deep Learning: Review of Multi-layer Perceptrons, Backpropagation Algorithms, Stochastic Gradient Descent, Loss and Activation functions, Regularization strategies, Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-Term Memory Units (LSTM), Auto encoders; Reinforcement Learning: Introduction to Reinforcement Learning, Multi-armed Bandit Problem, Finite Markov Decision Processes, Dynamic Programming, Eligibility Traces, Policy Gradient Methods, Deep-Q Learning; Applications and Case Studies.

Texts / References:

1. J. Shawe-Taylor and Nello Cristianini, Kernel Methods for Pattern Analysis, Cambridge University Press, 2004.
2. D. Koller and N. Friedman, Probabilistic Graphical Models – Principles and Techniques, MIT Press, 2009.
3. I. Goodfellow, Y. Bengio , A. Courville, Deep Learning, MIT Press, 2017
4. R. Sutton, Reinforcement Learning – An Introduction, MIT Press, 1998