# Stat 406 Spring 2007

• Lectures MWF 1-2, Math building 105
• Lab: Wed 4-5pm, Stat 310
• Office hours: Wed 2pm, Stat 308d
• Instructor: Kevin Murphy
• TA: Virginia Chen
• Syllabus
• Grading: Midterm: 30%, Final: 45%, Weekly ssignments: 25%
• Textbook. Required: Pattern Recognition and Machine Learning, Christopher M. Bishop, Springer 2006.
Recommended: The Elements of Statistical Learning, Trevor Hastie, Robert Tibshirani, Jerome Friedman, Springer 2001.
Recommended: All of statistics, Larry Wasserman, Springer 2004
Recommended: Pattern Classification (2nd ed.), Duda, Hart, Stork, Wiley 2001.
We will also use handouts.
• pre-requisites: cpsc 340 (machine learning) or stat 306 (finding relationships in data). You should be familiar with linear algebra, multivariate calculus, probability theory and basic programming (we will use Matlab).

## Timetable

Reading material is abbreviated as follows: B = Bishop book, H = Hastie book, M = Murphy book.

L1 Mon Jan 8
lec1.pdf B 1.1-1.2 hw1.pdf
L2 Wed Jan 10
Intro contd Matlab tutorial.pdf
More Matlab tutorials
lab1.pdf, lab1Data.zip, Clarifications,
L3 Fri Jan 12
Bayesian classifiers for Gaussian data Generative classifiers, B1.5, B2.1, B2.2, B4.2 .
L4 Mon Jan 15
cont'd M 12.1 hw2.pdf Download Code.zip, CodeEx.zip, Data.zip. You will need these files for this and future homeworks.
L5 Wed Jan 17
MLEs for 1D Gauss and Multinomial M 12.2.1, M 4.2.3.1, M.2.3.2, M 4.2.3.4 .
L6 Fri Jan 19
MLEs for MVN M 12.2.2-12.2.3, M 4.2.3.5 .
L7 Mon Jan 22
Decision boundaries M 12.2.5 .
L8 Wed Jan 24
Naive Bayes M 12.3 hw3.pdf
L9 Fri Jan 26
Classifiers with missing data/ Marginals and conditionals of a MVN M 3.4.2.1, 3.4.2.7, 3.4.2.10 .
L10 Mon Jan 29
Bayesian estimation of a 1D Gaussian mean M 5.3.1 .
L11 Wed Jan 31
Shrinkage estimate of 1D Gaussian mean M 5.7.1 .
L12 Fri Feb 2
cont'd . hw4.pdf due Monday 12th. You will also need gaussClassifMissingData.mat and Monday's handout (see L13)
L13 Mon Feb 5
Beta-binomial model M 5.2 .
L14 Wed Feb 7
EB for BB model Empirical Bayes for Beta-Binomial model .
L15 Fri Feb 9
Netflix (guest lecture from Hoyt Koepke) . .
L16 Mon Feb 12
Midterm Review .
L17 Wed Feb 14
Review cont'd . .
L18 Fri Feb 16
. . .
L19 Mon Feb 19
Spring break . .
L20 Wed Feb 21
Spring break . .
L21 Fri Feb 23
Spring break . .
L22 Mon Feb 26
Midterm . .
L23 Wed Feb 28
Least squares Linear regression .
L24 Fri Mar 2
Ridge regression . hw5.pdf, sinusoidData.mat, standardizeCols.m, degexpand.m,
L25 Mon Mar 5
Ridge regression . .
L26 Wed Mar 7
Ridge regression . .
L27 Fri Mar 9
Generalized ridge regression . .
L28 Mon Mar 12
Bayesian linear regression . hw6.pdf, pcaFaceDemo.m, sqdist.m, pcaHighDim.m, faceOlivetti_trainTest.mat, faceOlivetti_trainTestV6.mat (for people who do not have Matlab 7)
L29 Wed Mar 14
PCA pcaHandout.pdf .
L30 Fri Mar 16
Probabilistic PCA . .
L31 Mon Mar 19
PPCA . hw7.pdf, code.zip pcaFast.m,
L32 Wed Mar 21
No class . .
L33 Fri Mar 23
No class . .
L34 Mon Mar 26
Latent Semantic Indexing LSI .
L35 Wed Mar 28
Gaussian mixture models Mixture models .
L36 Fri Mar 30
EM . hw8.pdf, lsiDocuments.pdf, lsiMatrix.txt, lsiWords.txt
L37 Mon Apr 2
K means . .
L38 Wed Apr 4
EM for Bernoulli mixtures . .
L39 Fri Apr 6
Good Friday . .
L40 Mon Apr 9
Easter Monday . .
L41 Wed Apr 11
Review . .
Final: April 27th, 12 noon, Math 105.