CP 218, Theory and Applications of Bayesian Learning (Jan-April 2022), IISc Bangalore
Instructor: Punit Rathore
Credits: 3 Credits (2:1)
Class timing: Tue/Th, 10:00-11:30 am
Pre-requisites: A basic course on Probability and Linear Algebra (Mandatory), Fundamental Machine Learning (optional)
Course Descriptions: Probability Distributions, Maximum Likelihood and MAP Estimation, Conjugate Priors, Bayesian Regression and Classification, Expectation-Maximization, Bayesian Cluster Analysis, Bayesian Belief Networks, Model Selection, Probabilistic Graphical Models (PGMs), Probabilistic and Statistical Inferencing, Bayesian Estimation, Variable Elimination, Structure Learning, Gaussian Process, Bayesian Optimization, Markov Random Fields, Variational Inference and LDA, Markov Chain Monte Carlo and sampling algorithms, Bayesian Neural Networks, PGM examples and applications (including some industry and smart cities applications)
Course Objective: This course will involve mathematics, practical hands-on workshops, and programming tasks that require you to exercise critical thinking. It aims to build a strong foundation in the area of Bayesian techniques for machine learning. I hope that you will find this course and programming tasks intellectually stimulating and relevant to your current program and your future careers (research/industry).
Previous Teaching (During my Ph.D.)
Postgraduate Course COMP90051 Statistical Machine Learning with Assoc. Prof. Benjamin Rubinstein, School of Computing and Information Systems, The University of Melbourne, Australia, July 2018 – Nov 2018
Postgraduate Course SIT 743 Multivariate and Categorical Data Analysis with Asst. Prof. Sutharshan Rajasegarar, School of Information Technology, Deakin University, Burwood, Australia, July 2018 – Oct 2018
Postgraduate Course COMP90051 Statistical Machine Learning with Assoc. Prof. Trevor Cohn, School of Computing and Information Systems, The University of Melbourne, Australia, July 2016 – Nov 2016