Final exam: Apr 22, 8:30-11am am, MATH 103
Grading: midterm 35%, final 40%, homewokrs 25%.
Synoposis: This is a senior-level undergraduate class on machine learning, covering the foundations, such as (Bayesian) statistics and information theory, and then focusing on supervised learning (classification, regression).
Textbook: Draft copies of my textbook, Machine Learning: a probabilistic approach, will be made available for purchase on Jan 2nd, 2010, for $56.50, from CopieSmart in the UBC Village, between MacDonald's and the food court.
Abbreviated version for final exam is here
Pre-requisites. Linear algebra, calculus, probability theory, programming (preferably R or Matlab), some previous class on machine learning (eg CS 340) or applied statistics (eg Stat 306).
TA: Pavel Krupski p.krupskii@stat.ubc.ca
TA office hours: Tue 11-12, 312 LSK
Instructor Office hours: Fri 2-3, Stat 308d, or by appointmentIf you are registered, please join google groups which will be used for class-related announcements and discussions.
| L# | Date | Topic | Reading | Homework |
|---|---|---|---|---|
| L1 | Mon Jan 4 |
Admin, quiz | . | hw1.pdf, due Mon 11th. See also Getting started in Matlab for help. |
| L2 | Wed Jan 6 |
Quiz postmortem, classification | 1.1.1 | . |
| L3 | Fri Jan 8 |
KNN classifiers, cross validation | 1.4.2.1 | . |
| L4 | Mon Jan 11 |
Linear regression | 1.1.3- 1.1.4, 11.3 | . |
| L5 | Wed Jan 13 |
Linear regression cont'd | 11.3 | hw2.pdf, due Wed 20th |
| L6 | Fri Jan 15 |
Ridge regression | 11.4-11.5 | . |
| L7 | Mon Jan 18 |
Robust regression | 11.6, robust regression handout | . |
| L8 | Wed Jan 20 |
Logistic regression | 12.1-12.2 | hw3, due wed 27. spamData.mat |
| L9 | Fri Jan 22 |
Log. reg. cont'd | 12.3, logistic regression handout | . |
| L10 | Mon Jan 25 |
Neural nets | 14.1 | . |
| L11 | Wed Jan 27 |
Neural nets | 14.2, Neural nets handout | . |
| L12 | Fri Jan 29 |
Naive Bayes classifiers | Generative classifiers handout. | hw4,due Fri Feb 5 |
| L13 | Mon Feb 1 |
Naive Bayes classifiers | . | . |
| L14 | Wed Feb 3 |
Discriminant analysis | . | . |
| L15 | Fri Feb 5 |
Discriminant analysis | Regularized discriminant analysis | hw5,due Fri Feb 2 |
| L16 | Mon Feb 8 |
Mixture models | Mixture models (5feb2010 version) | . |
| L17 | Wed Feb 10 |
Mixture models | . | . |
| L18 | Fri Feb 12 |
Mixture models | . | . |
| L19 | Mon Feb 15 |
olympics | . | . |
| L20 | Wed Feb 17 |
olympics | . | . |
| L21 | Fri Feb 19 |
olympics | . | . |
| L22 | Mon Feb 22 |
olympics | . | . |
| L23 | Wed Feb 24 |
olympics | . | . |
| L24 | Fri Feb 26 |
olympics | . | . |
| L25 | Mon Mar 1 |
Review | Shortened edition 19feb10 version of book, only contains topics for the midterm (you can ignore starred sections) | . |
| L26 | Wed Mar 3 |
. | . | . |
| L27 | Fri Mar 5 |
Midterm | . | . |
| L28 | Mon Mar 8 |
L1 regularization | sec 4.4 (19feb) | . |
| L29 | Wed Mar 10 |
L1 regularization | sec 4.4 (19feb) | . |
| L30 | Fri Mar 12 |
Sparse Kernel machines | Sparse kernel machines handout | hw6,due Mon 22 |
| L31 | Mon Mar 15 |
Kernel machines | . | . |
| L32 | Wed Mar 17 |
Kernel machines | Updated Sparse kernel machines handout | . |
| L33 | Fri Mar 19 |
No class | . | . |
| L34 | Mon Mar 22 |
Inference in Gaussian models | Gaussian handout | . |
| L35 | Wed Mar 24 |
Linear Gaussian systems | . | . |
| L36 | Fri Mar 26 |
Number game, Beta-Binomial model | Bayesian stats 1 handout | . |
| L37 | Mon Mar 29 |
Summarizing posteriors | . | . |
| L38 | Wed Mar 31 |
Bayesian hypothesis testing | . | hw7,due Wed 7th |
| L39 | Fri Apr 2 |
Good Friday | . | . |
| L40 | Mon Apr 5 |
Easter Monday | . | . |
| L41 | Wed Apr 7 |
Bayesian inference for Gaussians | Bayesian stats 1 handout (7 apr version), Bayesian stats 2 handout (7 apr version) | hw8, due Wed 14th |
| L42 | Fri Apr 9 |
Bayesian inference for linear and logistic regression | Bayes2 handout | . |
| L43 | Mon Apr 12 |
Decision theory | Ch 10 of 2jan10 version | . |
| L44 | Wed Apr 14 |
Review session | . | . |