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||Monte Carlo Methods||Lecture 13||
|14||10/23||Markov Chain Monte Carlo Methods||Lecture 14||
|15||10/25||Review before Midterm||Lecture 15||
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.