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ubc.courses.cpsc.340.
Tutorial T1A Thur 3.30-4.30,
Frank Forward Building (behind Barn) room 317 (TA Hoyt)
Tutorial T1B Wed 4-5,
MacLeod 214 (TA Erik)
Instructor: Kevin Murphy.
Office hours: Tuesdays 4-5.
Office hours for December:
TAs: Hoyt Koepke hoytak@cs.ubc.ca, Erik Zawadzki faydorn@gmail.com.
Final syllabus.txt. Chapters refer to my MLABA book, 16 Nov 07 version.
Textbook: none required, but Pattern Recognition and Machine Learning by Chris Bishop and The Elements of Statistical Learning by Hastie, Tibshirani and Friedman are both recommended (although are more advanced than the level of this course).
| L# | Date | Slides | Reading | Homework |
|---|---|---|---|---|
| L1 | Wed Sep 5 |
Intro |
Optional:
|
. |
| L2 | Fri Sep 7 |
Classification and model selection | Probability theory refresher | . |
| L3 | Mon Sep 10 |
Matlab tutorial by Hoyt |
|
hw1.pdf, hw1code.zip, due Wed 19 |
| L4 | Wed Sep 12 |
k Nearest neighbors | . | . |
| L5 | Fri Sep 14 |
kNN cont'd | . | . |
| L6 | Mon Sep 17 |
Information theory | . | . |
| L7 | Wed Sep 19 |
Decision theory | . |
hw2.pdf,
hw2Code.zip, due Fri 28 |
| L8 | Fri Sep 21 |
Bayesian concept learning | Bayesian concept learning (sec 3 is optional) | . |
| L9 | Mon Sep 24 |
Bayesian concept learning | . | . |
| L10 | Wed Sep 26 |
Bayesian statistics 1 | Bayesian statistics - a concise introduction | . |
| L11 | Fri Sep 28 |
Bayesian statistics 2 | . | hw3.pdf,
hw3Code.zip, due Fri 5 |
| L12 | Mon Oct 1 |
Bayesian statistics 3 | Normal Gamma model, to replace sec 2.5 (NIX model) of Bayesian stats handout | . |
| L13 | Wed Oct 3 |
Bayesian model selection/ Frequentist parameter estimation |
|
. |
| L14 | Fri Oct 5 |
Review session | . | . |
| L15 | Mon Oct 8 |
Thanksgiving | . | . |
| L16 | Wed Oct 10 |
Midterm | . | . |
| L17 | Fri Oct 12 |
Midterm postmortem and ROC curves | ROC curves | . |
| L18 | Mon Oct 15 |
Naive Bayes classifiers | . | hw4.pdf,
hw4Code.zip, due Fri 26 |
| L19 | Wed Oct 17 |
Naive Bayes classifiers | . | . |
| L20 | Fri Oct 19 |
Naive Bayes classifiers | . | . |
| L21 | Mon Oct 22 |
Bayes nets 1 | . | . |
| L22 | Wed Oct 24 |
Class cancelled | . | . |
| L23 | Fri Oct 26 |
Bayes nets 2 | . | . |
| L24 | Mon Oct 29 |
Bayes nets 2 | . | hw5.pdf, due Fri 9.
You will also need qmrStub.m. |
| L25 | Wed Oct 31 |
Bayes nets 2 | . | . |
| L26 | Fri Nov 2 |
QMR | . | . |
| L27 | Mon Nov 5 |
Causality | . | . |
| L28 | Wed Nov 7 |
BN3 | Bayes nets handout | . |
| L29 | Fri Nov 9 |
Mixtures of Dirichlets | . | . |
| L30 | Mon Nov 12 |
Remembrance day holiday | . | . |
| L31 | Wed Nov 14 |
Gibbs sampling | . | . |
| L32 | Fri Nov 16 |
Gibbs sampling | . | . |
| L33 | Mon Nov 19 |
Gaussian classifiers | . | hw6.pdf, hw6Code.zip, due wed 28 |
| L34 | Wed Nov 21 |
Gaussian classifiers | . | . |
| L35 | Fri Nov 23 |
Markov models (Markov handout sec 1, 2.1-2.5) | . | . |
| L36 | Mon Nov 26 |
Markov models (Markov handout sec 2.5, 3) | Markov models. This replaces ch 12 of the book. You can skip sec 2.5, 4.4 and 5. | . |
| L37 | Wed Nov 28 |
Language models | . | . |
| L38 | Fri Nov 30 |
Review session | Overview of machine learning | . |