ECE 6424 - Probabilistic Graphical Models and Structured Prediction (3C)

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.

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.