CS340 (Machine learning) Fall 2006

Lectures MWF 4-5, Dempster 301

Instructor: Kevin Murphy



Discussion sections

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Office hours



Required (primary textbook):

Recommended additional reading



We will follow Alpaydin's book (for the most part), with some supplemenary handouts. The class will cover supervised learning (classification and regression) and unsupervised learning (density estimation, i.e., clustering, dimensionality reduction). Applications/examples will be drawn from computer vision, computational biology, speech/language, etc. The class will involve a fair amount of math (linear algebra, calculus and basic probability/ statistics), but also a fair amount of hands-on coding (in Matlab).



Reading material is abbreviated as follows: A = Alpaydin book, B = Bishop book, H = Hastie book.

Lecture notes include material borrowed from a variety of authors. Please contact me before copying.

L# Date Slides Reading Homework
L1 Wed Sep 6
Intro .
L2 Fri Sep 8
Intro A chap 1 .
L3 Mon Sep 11
Binary classification A 2.1, 2.4, 2.5, 2.7, 2.8 HW1: complete these matlab exercises by Mon Sep 18. The data is here. Clarifications

L4 Wed Sep 13
Learning theory A 2.2, A 2.3 .
L5 Fri Sep 15
Learning theory .
L6 Mon Sep 18
Bayesian concept learning HW2 by Mon Sep 25. The data/code is here. HW2 clarifications.
L7 Wed Sep 20
Bayesian concept learning Refresher on probability. .
L8 Fri Sep 22
Bayesian concept learning . .
L9 Mon Sep 25
Concept learning; clustering . HW3 by Mon Oct 2. The data/code is here. HW3 clarifications
L10 Wed Sep 27
Modeling discrete data with Bernoulli and multinomial distributions .
L11 Fri Sep 29
Modeling discrete data with Bernoulli and multinomial distributions Optional: In praise of Bayes, Economist article, Sept 2000 .
L12 Mon Oct 2
Naive Bayes classifiers Naive Bayes classifiers HW4 by Wed Oct 11. Code/data is here.
L13 Wed Oct 4
Naive Bayes classifiers Optional: Better Bayesian [spam] filtering, Paul Graham, Jan 2003 .
L14 Fri Oct 6
Markov models .
L15 Mon Oct 9
Thanksgiving . .
L16 Wed Oct 11
Markov chains . HW5 due Wed Oct 18. Code/data is here.
L17 Fri Oct 13
Information theory Information theory .
L18 Mon Oct 16
Info theory . .
L19 Wed Oct 18
Info theory . No HW!
L20 Fri Oct 20
Review session . .
L21 Mon Oct 23
Midterm . .
L22 Wed Oct 25
Midterm postmortem Notation used so far in class. .
L23 Fri Oct 27
L24 Mon Oct 30
MCMC . hw6.pdf, hw6Code.zip,
L25 Wed Nov 1
MCMC . .
L26 Fri Nov 3
L27 Mon Nov 6
MRFs . .
L28 Wed Nov 8
Simulated annealing (hw 6) . .
L29 Fri Nov 10
Variable elimination . hw7.pdf, hw7Code.zip,
L30 Mon Nov 13
Remembrance day holiday . .
L31 Wed Nov 15
Bayes nets Inference in graphical models .
L32 Fri Nov 17
Applications of Bayes nets Bayes nets hw8.pdf
L33 Mon Nov 20
Plates and param learning A 3.7, Microsoft's mobile manager .
L34 Wed Nov 22
Causality Simpson's paradox (extracted from the bn.pdf file) .
L35 Fri Nov 24
Gaussians Gaussians, A 5.2-5.4 .
L36 Mon Nov 27
Snow day . .
L37 Wed Nov 29
Gaussian mixtures Mixture models, A ch 7 .
L38 Fri Dec 1
Review session . .
- Mon Dec 11
Final exam DMP 110 3.30-6pm .