Prerequisite
Probability and machine learning
Course Contents
The course will tentatively cover various topics on Bayesian data analysis such as
- Maximum Likelihood (ML), maximum a-posteriori (MAP) and Bayesian estimation of single and multi-parameter models.
- Bayesian machine leaning approaches such as Bayesian linear regression, Bayesian Naïve Bayes Classifier, Bayesian logistic regression, Bayesian gaussian mixture modelling, latent Dirichlet allocation.
- Hierarchical modelling, Model selection.
- Inference algorithms based on Monte Carlo methods, Laplace approximation, variational inference, and expectation propagation.
- Bayesian non-parametrics (Gaussian Processes, Dirichlet Processes)
- Bayesian deep learning (Bayesian neural networks, variational auto-encoders, diffusion models)
- Bayesian optimization
About the Instructor
Dr. Srijith P.K is an Assistant Professor in the Department of Computer Science & Engineering, Artificial Intelligence, Ph.D.: IISc, Bengaluru IIT Hyderabad. Her research interest is in Probabilistic machine learning, Bayesian learning, Deep learning, AI/ML, Theoretical Computer Science. Link for Research Profile: https://scholar.google.co.in/citations?user=C1YpEWsAAAAJ&hl=en
Instructor ProfileCourse Assessment
Assessment may consist of assignments &/or quizzes &/viva &/or exams.