Probabilistic Graphical Models

January, 2020

Probabilistic Graphical Models refers to concise representations of probability distributions using graphs. It also studies efficient algorithms for sampling distributions represented in such form. Sampling might need to be done from the joint probability distribution, the marginals or even conditional distributions. Other algorithmic questions involve computing the Maximum Likelihood Estimate (MLE), Maximum Aposteriori Estimate (MAP) etc. This topic has deep connections and applications to various fields including Theoretical Computer Science, Machine Learning, Statistical Physics, Bioinformatics etc. We will also be covering analysis of Markov Chain Monte Carlo (MCMC) Algorithms.

Broadly the course will cover four modules

  1. Representations
  2. Inference
  3. Learning
  4. Advanced Topics (More on MCMC Methods, Normalizing Flows, Learning theory)

Draft Syllabus

Grading

Type of Eval –Weightage
Quiz 1 10
Mid Sem 15
Quiz 2 10
End Sem 25
Assignments 20
Project 20

Lectures

Textbook and References