Lecture | Date | Topic | Lecture notes | Reference (book sections) |
---|---|---|---|---|

1 |
January 26 |
Introduction |
notes | |

2 |
January 31 |
Linear regression, basis functions, least squares |
notes | 1.1, 3.1 |

3 |
February 2 |
Special DSI lecture, Chris Danforth (UVM) |
||

4 |
February 7 |
Maximum likelihood view of linear regression, outliers |
notes | 3.1 |

5 |
February 14 |
Robust regression and Linear Programming |
pff's notes, regular notes | |

6 |
February 16 |
Classification, Bayesian Decision Theory |
notes | 1.5 |

7 |
February 23 |
Estimating distributions (parametric and non-parametric) |
notes | 2.1, 2.2, 2.3, 2.5 |

8 |
February 28 |
Parzen windows, Bayesian estimation, predictive distribution |
notes | 2.1, 2.2, 2.3, 2.5 |

9 |
March 2 |
Linear separators, Perceptron Algorithm |
notes | 4.1, 4.1.7 |

10 |
March 7 |
Max-margin separator, linear support vector machines |
notes | 7.1 |

11 |
March 9 |
Gradient descent for linear SVM, Multiclass problems |
notes | 7.1 |

12 |
March 16 |
Kernel methods |
notes | 6 |

13 |
March 21 |
PAC learning |
notes | |

14 |
March 23 |
PAC learning |
notes | |

15 |
April 4 |
clustering, K-means |
notes | 9 |

16 |
April 11 |
Mixture of Gaussians, EM |
notes | 9 |

17 |
April 13 |
Principal Component Analysis |
notes | 12 |

18 |
April 18 |
Random projections |
notes | |

19 |
April 20 |
Guest lecture, Katherine Kinnaird |
||

20 |
April 25 |
Neural Networks |
notes | 5 |

21 |
April 27 |
Neural Networks |
notes | 5 |