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
Topic 4, Linear Models, Conditional Random Fields
Topic 5, Graphical Models and Structured SVMs
Topic 6, Beyond Junction Tree: High Tree-Width Models
Topic 7, Kernels and Probabilistic Kernels
Topic 8, Feature and Kernel Selection
Topic 9, Meta-Learning and Multi-Task SVMs
Topic 10a, Semi-Supervised Learning
Topic 10b, Graph-Based Semi-Supervised Learning
Topic 11, Clustering
Topic 11b, Clustering (Proof)
Topic 12, Boosting PPT File