Probabilistic Graphical Models - Spring 2024

Instructor

Dr. Behrooz Nasihatkon

Teaching Assistants

Sara Farahani

Milad Gholamrezanejad

Resources

D. Koller and N. Friedman, Probabilistic Graphical Models: Principles and Techniques

Michael I. Jordan, An Introduction to Probabilistic Graphical Models

Goodfellow, Ian, Yoshua Bengio, and Aaron Courville. Deep learning. MIT press, 2016.

Wainwright, Martin J., and Michael I. Jordan. "Graphical models, exponential families, and variational inference." Foundations and Trends in Machine Learning 1.1–2 (2008): 1-305.

Nowozin, Sebastian, and Christoph H. Lampert. "Structured learning and prediction in computer vision." Foundations and Trends in Computer Graphics and Vision 6.3–4 (2011): 185-365.

Session	Topic	Course Material	Videos
1	Logistics, Intro, Problems	Lecture 1a- Slides Lecture 1b-Slides	Lecture 1- Aparat Lecture 1- Youtube
2	Probabilistic Modeling, Generative Models, Complexities	Lecture 2- Slides	Lecture 2- Aparat Lecture 2- Youtube
3	Independence Reduces Complexity	Lecture 3- Slides	Lecture 3- Aparat Lecture 3- Youtube
4	Conditional Independence	Lecture 4- Slides	Lecture 4- Aparat Lecture 4- Youtube
5	Bayesian Nets	Lecture 5-Slides	Lecture 5- Aparat Lecture 5- Youtube
6	Markov Random Fields	Lecture 6-Slides Lecture 6- Notes	Lecture 6- Aparat Lecture 6- Youtube
7	Flow of Influence, I-MAP	Lecture 7-Slides Homework 1	Lecture 7- Aparat Lecture 7- Youtube
8	Representing CPDs	Lecture 8- Slides Lecture 8- Notes	Lecture 8- Aparat Lecture 8- Youtube
9	Continuous CPDs, Temporal Models	Lecture 9- Slides Lecture 9- Notes	Lecture 9- Aparat Lecture 9- Youtube
10	Parameter Sharing, MRF Representation	Lecture 10- Slides Lecture 10- Notes	Lecture 10- Aparat Lecture 10- Youtube
11	Conditional Random Fields	Lecture 11- Slides	Lecture 11- Aparat Lecture 11- Youtube
12	Introduction to Inference, Variable Elimination	Lecture 12- Slides	Lecture 12- Aparat Lecture 12- Youtube
13	Junction Tree Message Passing, Belief Propagation in Junction Trees	Lecture 13- Slides Lecture 13- Notes Homework 2	Lecture 13- Aparat Lecture 13- Youtube
14	Loopy Belief Propagation	Lecture 14- Slides Lecture 14- Notes	Lecture 14- Aparat Lecture 14- Youtube
15	MAP Inference, Max Marginals	Lecture 15- Slides Lecture 15- Notes	Lecture 15- Aparat Lecture 15- Youtube
16	Map Inference, Max-sum VE and Message Passing, Graph Cuts	Lecture 16- Slides Lecture 16- Notes Homework 3	Lecture 16- Aparat Lecture 16- Youtube
17	Recursive Bayesian Filtering, Intro to Kalman Filter	Lecture 17- Slides Lecture 17- Notes	Lecture 17- Aparat Lecture 17- Youtube
18	Kalman Filter, Linearization and Extended Kalman Filter, Unscented Kalman Filter, Particle Filter	Lecture 18- Slides Lecture 18- Notes Homework 4	Lecture 18- Aparat Lecture 18- Youtube
19	Introduction to Sampling, Approximate Inference with Sampling	Lecture 19- Slides	Lecture 19- Aparat Lecture 19- Youtube
20	Sampling from Bayes Nets, Introduction to Markov Chain Monte Carlo (MCMC) methods	Lecture 20- Slides Lecture 20- Notes	Lecture 20- Aparat Lecture 20- Youtube
21	Markov Chain Monte Carlo (MCMC), Gibbs Sampling	Lecture 21- Slides Lecture 21- Notes	Lecture 21- Aparat Lecture 21- Youtube
22	Introduction to Learning, Parameter Learning in Bayesian Networks	Lecture 22- Slides Lecture 22- Notes	Lecture 22- Aparat Lecture 22- Youtube
23	MRF Parameter Learning	Lecture 23- Slides Lecture 23- Notes	Lecture 23- Aparat Lecture 23- Youtube
24	MCMC-learning, Contrastive Divergence CRF Parameter Learning	Lecture 24- Slides Lecture 24- Notes Homework 5	Lecture 24- Aparat Lecture 24- Youtube
25	Learning with Incomplete Data, Latent Variable Models the Expectation-Maximization Algorithm	Lecture 25- Slides Lecture 25- Notes	Lecture 25- Aparat Lecture 25- Youtube
26	Variational Inference, Mean Field	Lecture 26- Slides Lecture 26- Notes	Lecture 26- Aparat Lecture 26- Youtube
27	Variational Inference on Latent Variable Models, ELBO	Lecture 27- Slides Lecture 27- Notes	Lecture 27- Aparat Lecture 27- Youtube
28	Generative Models, Restricted Boltzman Machines, Deep Belief Networks	Lecture 28- Slides Lecture 28- Notes	Lecture 28- Aparat Lecture 28- Youtube
29	Variational Autoencoders	Lecture 29- Slides Lecture 29- Notes	Lecture 29- Aparat Lecture 29- Youtube
30	Generative Adversarial Networks	Lecture 30- Slides Lecture 30- Notes	Lecture 30- Aparat Lecture 30- Youtube
31	Normalizing Flows	Lecture 31- Slides	Lecture 31- Aparat Lecture 31- Youtube
32	Diffusion Models	Lecture 32- Slides Final Project	Lecture 32- Aparat Lecture 32- Youtube

Exams

Midterm	Final
Questions File	Questions File