The class will cover topics such as Directed/Undirected graphical models, template models, Inference (variable elimination and sum-product message passing), Learning (Maximum Likelihood Estimation, Generalized Linear Models, learning over fully/partially observed data etc.), approximate inference (MCMC methods, Gibbs sampling). We will also dive to more research oriented topics such as scalable implementations of graphical models, connections of graphical models and relational representation learning, and applications of graphical models to problems in data management (such as data integration and data cleaning).
The textbooks we will use are the following two:
You should have taken an introductory machine learning course. You should understand basic probability and statistics, and college-level algebra and calculus. For example it is expected that you know about standard probability distributions (Gaussians, Poisson), and also how to calculate derivatives.
|#||Date||Topic||Lecture Materials||Reading Material||Assignments|
|Introduction and Class Overview|
|1||9/6||Introduction to Graphical Models||Lecture 1|
|2||9/11||Directed Graphical Models: Bayesian Networks||Lecture 2||
|3||9/13||Undirected Graphical Models||Lecture 3||
|4||9/18||Variable Elimination||Lecture 4||
|5||9/20||Clique Trees and Message Passing||Lecture 5||
|6||9/25||Learning over Generalized Linear Models||Lecture 6||
Homework 1: Due Oct 2nd by 2:30 p.m. (beginning of class)
|7||9/27||Learning BNs||Lecture 7||
|8||10/2||Learning Undirected Graphical Models||Lecture 8||
|9||10/4||Structure Learning||Lecture 9||
|10||10/9||Learning with Partially Observed Data-The Expectation Maximization Algorithm||Lecture 10||
|11||10/11||Loopy Belief Propagation||Lecture 11||
Homework 2: Due Oct 25th by 2:30 p.m. (beginning of class)
|12||10/16||Mean Field Approximation||Lecture 12|
|13||10/18||Variational Inference Continued||Lecture 13|
|14||10/23||Sampling Methods to Approximate Inference||Lecture 14||
|15||10/25||Review before Midterm||Review||
|Advanced Graphical Models|
|17||11/01||Spectral Learning for GMs||Lecture 16||
|18||11/06||Markov Logic Networks||Lecture 17|
|19||11/08||Deep Learning and Graphical Models||Lecture 18||
||Project Proposal Due|
|20||11/13||Deep Learning Models: Autoencoders and Variational Autoencoders||Lecture 19|
|20||11/15||Deep Learning Models: Generative Adversarial Networks||Lecture 20|
|21||11/20||Deep Learning Models: CNNs and RNNs||Lecture 21||
|22||11/27||Deep Learning Models: Attention and Transformers||Lecture 22||
||Project Mid Report|
|23||12/04||Applications of PGMs to Data Management||Lecture 23||
|25||12/06||No Class (Theo at DARPA)||
You are encouraged to discuss the homework assignments with other students; it's fine to discuss overall strategy and collaborate with a partner or in a small group, as both giving and receiving advice will help you to learn.
However, you must write your own solutions to all of the assignemtns, and you must cite all people you worked with. If you consult any resources outside of the materials provided in class, you must cite these sources.
If you do not do so, we will consider this a violation of the University of Wisconsin Honor Code.