ECE 6424 - Probabilistic Graphical Models and Structured Prediction
Advanced concepts in machine learning; focus on probabilistic graphical models and structured output prediction. topics include directed models (Bayes Nets), undirected models (Markov/Conditional Random Fields), exact inference (junction tree), approximate inference (belief propagation, dual decomposition), parameter learning (MLE, MAP< EM, max-margin), structure learning.
Why take this course?
We are witnessing an explosion in data - from billions of images shared online to Petabytes of tweets, medical records and GPS tracks, generated by companies, users and scientific communities. Unfortunately, real-world data are often noisy, ambiguous, and exhibit nontrivial or complex structure. Probabilistic and structured models provide a principled framework for dealing with uncertainty and for converting evidence from multiple nosy sources into a posteriori belief about the world.
A grade of C- or better in 5424G or CS 5824.
5424G and CS 5824: basic concepts of statistical learning I(likelihood, maximum likelihood, bias and variance, underfitting, overfitting, regularization, cross-validation); supervised and unsupervised learning algorithms.
Major Measurable Learning Objectives
Analyze and contrast board classes of probabilistic graphical models (Bayes Nets vs. Markov Nets; directed vs. undirected graphical models)
State and describe independence assumptions encoded by different models
Explain and implement algorithms for learning and inference (e.g. belief propagation)
Discuss and critique research papers on these topics
Identify open research questions in these areas
Percentage of Course
1. Bayesian Networks: a) Representation/Semantics; b) Parameter & Structure Learning; c) Inference
2. Markov Random Fields: a) Representation/Semantics; b) Exact Inference; c) Approximate Inference; d) Parameter Learning
3. Structured SVMs: a) Structured Hinge Loss; b) Cutting-Plane optimization (n-slack, 1-slack); c) Online optimization (Frank-Wolfe method)