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.
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.
Percentage of Course
|1. Bayesian Networks: a) Representation/Semantics; b) Parameter & Structure Learning; c) Inference||30%|
|2. Markov Random Fields: a) Representation/Semantics; b) Exact Inference; c) Approximate Inference; d) Parameter Learning||40%|
|3. Structured SVMs: a) Structured Hinge Loss; b) Cutting-Plane optimization (n-slack, 1-slack); c) Online optimization (Frank-Wolfe method)||30%|