Laboratory of Statistical Artificial Intelligence and Machine Learning

Probabilistic Graphical Models Spring 2023

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 12.00-12.50pm
Wednesday 10.00-10.50am
Thursday 12.00-12.50pm

Reference Material

Primary Textbook : Probabilistic Graphical Models: Principles and Techniques, Daphne Koller and Nir Friedman, MIT Press
Other Reference Books: Modeling and Reasoning with Bayesian Networks, Adnan Darwiche, Cambridge University Press.

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

Quizzes Two pre-announced quizzes will be conducted during the semester. Check the academic calendar for the quiz dates. Each quiz will account for 15% of the overall grade.

Assignments Two pre-announced assignments will be conducted during the semester. Each assignment will account for 15% of the overall grade. Assignment 1 is due during week 4 and assignment 2 is due during week 9

Exams There will be an end-semester exam that will account for 40% of the overall grade.

Attendance

This course follows the attendance criteria mandated by the institute.

Course Topics

Topics in Probability and Graph Theory Review
Bayesian Networks
Undirected Graphical Models
Exact Inference
Approximate Infeerence
Variational Inference
Learning Graphical Models
Sequence Models: Hidden Markov Models
Advanced Topics

Lecture Material

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