Lecture notes for Graphical Models, COMPSCI688, taught by Justin Domke at UMass Amherst in Spring 2021.
These notes were primarily the effort of Nilesh Khade, Abhishek Ahuja, Abhinav Agrawal, Ke Xiao, and Justin Domke. These also include some figures based on assistants in previous versions of the course.
There are no doubt typos still remaining in these notes, and I'd still like to fix them, so please leave a comment if you find any. Comments should be available to anyone (though no promises on latency in making fixes!)
01 - Introduction - Feb 2, 2021
02 - Directed Models - Feb 4, 2021
03 - Representation in Directed Models - Feb 9, 2021
04 - Conditional Independency and Maximum Likelihood Learning - Feb 11, 2021
05 - Maximum Likelihood in Directed Models - Feb 16, 2021
06 - Directed and Undirected Models - Feb 18, 2021
07 - Hammersley-Clifford Theorem, Markov Random Fields - Feb 23, 2021
08 - Conditional Random Fields - Feb 25, 2021
09 - Message Passing - March 2, 2021
10 - Message Passing - March 4, 2021
11 - Exponential Family - March 9, 2021
12 - Exponential Family (Continued) - March 11, 2021
13 - Learning in Exponential Families - March 16, 2021
14 - Maximum Likelihood Efficiency - March 18, 2021