align box 10-701 Machine Learning (Spring 2012)

Course Instructor: Ziv-Bar Joseph
School of Computer Science, Carnegie Mellon University

Course Schedule

Note: handouts, recitation slides and homeworks will be posted prior to the corresponding lectures.
 
Date Lecture Readings Handouts NB
Mon 1/23 Intro to ML and probability
Slides recitation (MATLAB1)
Mon 1/25 Density estimation Ch 2.0, 2.1, 2.3, 2.3.1 (Bishop)
 Slides
Mon 1/30 Classification Ch 1.5, 2.5.2 (Bishop)
 Slides recitation (MATLAB2)
Wed 2/1 Naive Bayes Ch 1.5 (Bishop)
 Slides
Mon 2/6 Regression Ch 3, 3.1 (Bishop)
 Slides recitation (distributions) class slides
Mon 2/13 Logistic regression Ch 4.3 (Bishop)
 Slides recitation (Bayes) Beta-binom model (Navarro and Perfors)
Wed 2/15 Learning theory 1 Ch 7 (Mitchell)
 Slides
Mon 2/20 Learning theory 2
Slides recitation (classifiers)
Wed 2/22 Decision Trees
Slides
Mon 2/27 Neural networks Ch 4 (Mitchell) and Chap 5, 5.2.3, 5.3 (Bishop)
Slides
Wed 2/29 Support vector machines
Slides recitation (decision trees)
Mon 3/5 Clustering 1
Slides recitation (neural networks)
Wed 3/7 Clustering 2
Slides
Wed 3/19 Semi-supervised learning
Slides
Mon 3/26 Bayesian networks 1
Slides
Wed 3/28 Bayesian networks 2 Chap 8.1 and 8.2.2 (Bishop)
Slides
Mon 4/2 Hidden Markov models 1
Slides recitation (spectral learning)
Wed 4/4 Hidden Markov models 2
Slides
Mon 4/9 Markov decision processes
Slides recitation (HMM); reading1 ; reading2
Wed 4/11 Reinforcement learning
Slides
Mon 4/16 Principal component analysis
Slides (demo) recitation;
Wed 4/18 Model and feature selection
Slides
Wed 4/23 Boosting
Slides