10715 Advanced Introduction to Machine Learning

Recitation Schedule

Recitations will be held on Tuesdays from 6pm - 7pm at 4211 GHC.

Date	Topic	Readings and useful links
Tue 9/16 GHC4211, 12 - 1pm	Sparsity, Lasso, Group Lasso, Nonparametric Regression Samy	Ryan's Notes on Sparsity Nonparametric Regression: Tsybakov Chapter 1.5, 1.6, 1.7 Fusso, Oliva, Poczos et al.
Tue 9/23 GHC4211, 6 - 7pm	KKT conditions Veeru	Geoff & Ryan's slides on KKT conditions Boyd and Vandenberghe Chap. 5
Tue 10/7 GHC4211, 6 - 7pm	Kernels and RKHS (basics) Veeru	Chap 2 in Learning with Kernels
Wed 10/29 GHC4102, 6 - 7pm	KDE, kNN for Density Estimation, Classification Factor Analysis Samy	KDE, kNN: Section 2.5 in Bishop, PRML Factor Analysis: Andrew Ng's notes
Tue 11/4 GHC4211, 6 - 7pm	Graphical Models Veeru
Tue 11/18 GHC4211, 6 - 7pm	Gaussian Processes GPs for Regression (review), Classification and Optimization Samy	Regression: Ch 2 in Rasmussen & Williams Classification: Ch 3 in Rasmussen & Williams Bayesian Optimization: Brochu et. al, A Tutorial on Bayesian Optimization
Tue 11/25 GHC4211, 6 - 7pm	Spectral Graph Theory for Clustering and Dimensionality Reduction Samy	Luxburg, 2007, A Tutorial on Spectral Clustering Belkin, Niyogi, 2002, Laplacian Eigenmaps for Dimensionality Reduction and Data Representation

Homework help sessions