Laboratory of Statistical Artificial Intelligence and Machine Learning

Probabilistic Graphical Models Fall 2025

Description

Probabilistic graphical models are a useful tool for representing domain knowledge using probability distributions. They have a wide range of applications in fields such as machine learning, computer vision, and natural language processing, The combination of graph theory and probability theory provides a rich framework for modeling domain knowledge consisting of a large number of random variables with interdependencies. This course will provide a thorough overview of the topic, including the key formalisms and techniques used to build, predict, and make decisions under uncertainty.

Lecture Timings (Tentative)

Tuesday 3.30-4.45pm
Thursday 2.00-3.15pm

Reference Material

Academic integrity

Students enrolled in this course are expected to exhibit a strong desire to learn, rather than just fulfilling a requirement for their degree. Engaging in discussions that help students better understand concepts or problems is encouraged. However, all submitted work must be original. Plagiarism, including copying from the internet, textbooks, or any other source for which the student does not hold the copyright, as well as sharing code with other students, will not be tolerated and will result in strict disciplinary action, including a failing grade in the course. If you have any questions about this policy, please contact the instructor. All academic integrity violations will be handled in accordance with institute regulations.

Grading Policy
  • Mid-Sem One Mid-Sem exam that will account for 30% of the overall grade.

  • Paper Presentations Research paper presentation that will account for 10% of the overall grade

  • Project Semester long project for 60% of the overall grade.

Attendance

This course follows the attendance criteria mandated by the institute.

Course Topics
  • Introduction

  • Bayesian Networks

  • Undirected Graphical Models

  • Exact Inference

  • Approximate Infeerence

  • Parameter Learning (MLE and EM)

  • Structure Learning

  • HMM

  • Variational Inference

  • Deep Generative Models

  • Deep Generative Models

  • Probabilistic Circuits

  • Advanced Topics

Lecture Material

Student enrolled in the course can access the lecture material from Google Classroom Link