CS 540 (Machine learning) Spring 2010 (term 2)

TR 9.30-11.
Tuesdays Forestry 1613 (next to Tim Hortons).
Thursdays Frank Forward building, room 303. Opposite the Barn on Main Mall.

New Office hours: Tue 3.30-4.30, CS 187, or by appointment

If you are registered, please join google groups which will be used for class-related announcements and discussions.

Matlab tutorial

Matlab software (pmtk3)

Data in Matlab format

Synoposis: This is a fast-paced graduate class on machine learning, covering the foundations, such as (Bayesian) statistics and information theory, as well as supervised learning (classification, regression), and unsupervised learning (clustering, dimensionality reduction, graphical models).

For a slower version of this class, which does not cover unsupervised learning, check out Stat406, also offered in Spring 2010.

Textbook: Draft copies of my textbook, Machine Learning: a probabilistic approach (MLAPA), is available for purchase for $56.50 from Copiesmart in the UBC Village (next to Macdonald's).

Pre-requisites. Linear algebra, calculus, probability theory, programming (preferably Matlab), some undergrad class on machine learning (eg CS 340) or statistics (eg Stat 306).

Grading: Midterm 35%, Homeworks 35%, project 30%

Projects page


In the table below, readings refer to sections from my textbook (2jan2010 edition).
L# Date Topic Reading Homework
L1 Tue Jan 5
Admin, intro to classif and regr, entrance quiz 1.1 hw1.pdf, due Tue 12th. See also Getting started in Matlab for help.
L2 Thu Jan 7
Knn, CV 1.4 .
L3 Tue Jan 12
Intro to unsupervised 1.2-1.3, 11.1-11.2 hw2.pdf, due Tue 19th.
L4 Thu Jan 14
Ridge, robust regression 11.3-11.5, robust regression handout .
L5 Tue Jan 19
Logistic regression 12.1-12.4, logistic regression handout .
L6 Thu Jan 21
Neural networks 14.1-14.2 hw3, due Thur 28th, spamData.mat,
L7 Tue Jan 26
EM for robust and probit regression; Naive Bayes classifiers EM for regression and classification handout .
L8 Thu Jan 28
Discriminant analysis Generative classifiers handout, and Fisher's LDA handout hw4, due Thur 4th
L9 Tue Feb 2
Missing data in generative classifiers Mixture models .
L10 Thu Feb 4
Mixture models Regularized discriminant analysis, EM for MAP estimation of mixtures, bankruptcy.txt data, needed for HW5, Fraley05, needed for HW5. hw5, due Thur 11th
L11 Tue Feb 9
L1 regularization Variable selection handout .
L12 Thu Feb 11
More on variable selection . .
L13 Tue Feb 16
olympics . .
L14 Thu Feb 18
olympics . hw6, due Tue 2nd
L15 Tue Feb 23
olympics . .
L16 Thu Feb 25
olympics . .
L17 Tue Mar 2
Review session . .
L18 Thu Mar 4
Midterm . .
L19 Tue Mar 9
Sparse kernel machines Sparse kernel machines handout .
L20 Thu Mar 11
Sparse kernel machines, Gaussians . hw7, due Tue 23rd. adultCensus.zip data
L21 Tue Mar 16
Bayesian inference Bayesian inference for single parameter models .
L22 Thu Mar 18
No class Bayesian stats 1 .
L23 Tue Mar 23
More Bayesian stats Bayesian stats 2 (26mar10 version, slightly updated from 22mar10 version). .
L24 Thu Mar 25
Empirical, hierarchical and variational Bayes Variational inference (26mar10 version, incomplete). See also Bishop's chapter on variational inference hw8, due Thur 1st.
L25 Tue Mar 30
More variational Bayes Handout on conjugate duality and constrained optimization, Handout on variational inference .
L26 Thu Apr 1
Graphical models . Hw8 due
L27 Tue Apr 6
HMMs and Kalman filters Handout on Kalman filters, Handout on HMMs 1 page project proposal due
L28 Thu Apr 8
Forwards-backwards algorithm, and friends . .
L29 Tue Apr 13
Monte Carlo inference Ch 20 of 2jan10 version of book .
L30 Thu Apr 15
Inference in graphical models Handout to appear later .
. Tue Apr 27
Project presentations . .