Office hours: Fri 1-2pm.

**If you cannot register**, but you feel you have the required background,
please send your student id number to
Joyce Poon (poon@cs.ubc.ca).
If you are from another UBC department,
fill out
this form.

Sign up at google groups to get email announcements etc.

If you do not have the pre-requisites, but are still interested in learning about machine learning, I recommend you take CS340, the undergrad version of this class, taught by Nando de Freitas Fall 2008.

If you cannot handle this, I recommend you take CS340, the undergrad version of this class.

Copiesmart Centre, 103-5728 U. Blvd, right next to McDonald's in the UBC Village.

If you find typos, please follow the procedure outline here.

In addition to my book, you may find the following useful:

- Pattern Recognition and Machine Learning, Chris Bishop, Springer 2006.
- The elements of statistical learning, Trevor Hastie, Robert Tibshirani and Jerome Friedman, Springer 2001.
- All of Statistics, Larry Wasserman, Springer 2004.
- Information theory, inference and learning algorithms, David Mackay, CUP 2003
- Bayesian Computation with R, Jim Albert, Springer 2007.
- Pattern Classification
(2nd ed.), Duda, Hart, Stork,
Wiley 2001.

- John Langford's blog
- Radford Neal's blog
- Andrew Gelman's blog

L# | Date | Topic | Reading | Homework |
---|---|---|---|---|

L1 | Tue Sep 9 |
Intro | Ch 1, Matlab tutorial | hw1.pdf |

L2 | Thu Sep 11 | Data visualization, probabilistic models, MLE | Ch 2 | . |

L3 | Tue Sep 16 | Basic concepts | New version of ch 2 | hw2.pdf prostate.mat (same as in BLT/Data). hw2Sol.pdf |

L4 | Thu Sep 18 | Linear regression | 19.2, 19.3, Review ch 38 | . |

L5 | Tue Sep 23 | Linear algebra, Ridge regression | 19.4, Review ch 38 | Hw3.pdf , hw3Sol.pdf |

L6 | Thu Sep 25 | Logistic regression | 22.1, 22.2 | . |

L7 | Tue Sep 30 | MVN, LDA/QDA | 3.2, 4.2 | hw4.pdf, naiveBayesExCode.zip, hw4Sol.pdf |

L8 | Thu Oct 2 | Naive Bayes; Beta-Binomial model | Ch 4, 9.3 | . |

L9 | Tue Oct 7 | Bayesian concept learning; Beta-Binomial; Dirichlet-Multinomial | 8.1-8.3, 9.1-9.4 | hw5.pdf, NBLRcode.zip |

L10 | Thu Oct 9 | Bayesian parameter estimation for Gaussians, generative classifiers, linear and logistic regression | 5.6, 22.1.3, 9.6 | . |

L11 | Tue Oct 14 | Decision theory ; model selection | New ch 5, new ch 6, new 3.3, new 8.6 | . |

L12 | Thu Oct 16 | Midterm |
. | . |

L13 | Tue Oct 21 | Feature selection | 20.1-20.3, 21.1-21.3 | . |

L14 | Thu Oct 23 | L1 regularization | . | . |

L15 | Tue Oct 28 | Mixture models, EM, non-parametric models | 3.3-3.4, 14.1-14.5, 17.1-1.3 | HW6 |

L16 | Thu Oct 30 | Guest lecture by Matt Brown on applications of non-parametric regression | . | . |

L17 | Tue Nov 4 | Directed graphical models | . | Project proposals due |

L18 | Thu Nov 6 | Conditioanl mixture models, sparse Bayesian learning, EM as bound optimization | . | . |

L19 | Tue Nov 11 | Remembrance day | . | . |

L20 | Thu Nov 13 | Kalman filters | . | . |

L21 | Tue Nov 18 | PCA | . | . |

L22 | Thu Nov 20 | Markov models | . | . |

L23 | Tue Nov 25 | HMMs | . | . |

L24 | Thu Nov 27 | MCMC | . | . |