Graphical Models

CS395T, Fall 2013
GDC 1.406, Mon & Wed 9:30 - 11:00

Home | Instructor | Syllabus | Homeworks | Projects

Module 1: Representation




Basics, Bayesian Networks


Bayesian Networks, Undirected Graphical Models


Chordal (Decomposable) Graphs, Conditional Random Fields (CRFs)


Chains, Trees, Factorial Graphs, Applications


Markov Properties


Module 2: Exact Inference




Variable Elimination, Sum Product


Multivariate Gaussian Distribution, Kalman Filtering, Smoothing


Hidden Markov Models, Forward Backward Algorithm


Junction Trees


Module 3: Approximate Inference




Exponential Families, Variational Optimization View of Inference


Message Passing revisited, Mean Field


Variational Methods - I


Variational Methods - II


Sampling Methods


Maximum A Posteriori (MAP)- I


Maximum A Posteriori (MAP)- II


Discriminative Inference


Mixture Models - I


Mixture Models - II


Module 4: Learning




Sparse Estimation Basics


Convex Optimization Based Methods - I


Convex Optimization Based Methods - II


Greedy Methods


Final Project Poster Presentation




Poster Presentation in Class