This page will contain information and class notes.
Also make sure to check the "papers" page for the weekly readings.
Topic 1, Linear/Gaussian Modeling, PCA
Topic 2, Nonlinear Dimension Reduction
Topic 3, Maximum Entropy Discrimination
Topic 4, Linear Models, Conditional Random Fields
Topic 5, Graphical Models and Structured SVMs
Topic 6, Kernels and Probabilistic Kernels
Topic 7, Feature and Kernel Selection
Topic 8, Meta-Learning and Multi-Task SVMs
Topic 9, Semi-Supervised Learning
Topic 9b, Graph-Based Semi-Supervised Learning
Topic 10, Beyond Junction Tree: Loopy Models
Topic 11, Clustering
Topic 12, Boosting PPT File