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Advanced Introduction to Machine Learning
10-715, Fall 2014Eric Xing,   Barnabas Poczos School of Computer Science, Carnegie-Mellon University |
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
- Homework 1: Tue 9/30
- Homework 2: Tue 10/14
- Midterm review session: Tue 10/21
- Homework 3: Tue 11/11
© 2014 Eric Xing @ School of Computer Science, Carnegie Mellon University
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