Prerequisites: COMS W4771 or permission. Background in linear algebra and statistics.

Description: Advanced machine learning tools with applications in perception and behavior modeling. Topics include Eigenspace Methods, Independent Components Analysis, Latent Models, Bayesian Inference, Kalman Filtering, Time Series Prediction, Hidden Markov Models, Dynamic Bayesian Networks, Markov Random Fields, Variational Methods, Support Vector Machines, Kernel Methods, Maximum Entropy, Transduction, Feature Selection, Meta-Learning, Dimensionality Reduction, Manifold Learning, and Spectral Clustering.

Bulletin Board: Class bulletin board (Click on Discussion)