Machine Learning

10-701/15-781, Spring 2011

Carnegie Mellon University

Tom Mitchell

Date Time Place Topic Handouts
Jan 19 5-6pm
GHC 6115 Probability Review Slides
Jan 26 5-6pm
NSH 3305 Naive Bayes Slides
Feb 3 1:30-2:50pm
Margaret Morrison A14 Review: logistic regression, Gaussian naive Bayes, linear regression, and their connections.

New materials: bias-variance decomposition, bias-variance tradeoff, overfitting, regularization, and feature selection
Feb 9 5-6pm
NSH 3305 Bayes Nets: Representation Slides
Feb 16 5-6pm
GHC 6115 Bayes Nets: Inference & D-Separation Slides
Feb 23 5-6pm GHC 6115 EM Algorithm and Midterm Exam Review EM Slides
Midterm Exam Review (Part 1)
Mar 2 5-6pm NSH 3305 Midterm Exam Review Midterm Exam Review (Part 2)
Mar 16 5-6pm NSH 3305 VC Dimensionality & Midterm Recap Slides
Mar 23 5-6pm NSH 3305 Recap: training, testing, true errors and overfitting.
PAC learning with finite hypothesis space
PAC learning with infinite hypothesis space (VC bounds)
Mistake bounds
Semi-supervised learning
Mar 30 5-6pm NSH 3305 HMM (Forward-Backward, Veterbi, EM for Learning), Neural Network Slides
Apr 6 5-6pm NSH 3305 Principal Components Analysis, Independent Component Analysis, Canonical Correlation Analysis, Fisher's Linear Discriminant, Topic Models and Latent Dirichlet Allocation. Slides
Apr 13 5-6pm NSH 3305 Support Vector Machines, Kernel Methods Slides
Apr 20 5-6pm NSH 3305 Active Learning Slides
Apr 27 5-6pm NSH 3305 Reinforcement Learning Slides
May 4 5-6pm NSH 3305 Final Review Slides