Supplementary references are to the following textbook:

Trevor Hastie, Robert Tibshirani, and Jerome Friedman, * The Elements of Statistical Learning* (2nd edition)

It is available online through Roger and is referred to below as HTF.

Sep 28 | Nearest neighbor classification [HTF 2.3, 7.10, 13.3] |

Oct 3 | Nearest neighbor classification, cont'd
A host of prediction problems [HTF 2.1, 2.2] |

Oct 5 | Probability review
Homework 1 due |

Oct 10 | Probability review, cont'd
Introduction to generative modeling |

Oct 12 | Introduction to generative modeling, cont'd
Homework 2 due Quiz 1 |

Oct 17 | Linear algebra primer |

Oct 19 | Gaussian generative models
Homework 3 due |

Oct 24 | Linear regression [HTF 2.3.1, 3.2, 3.4] |

Oct 26 | Logistic regression [HTF 4.4]
Homework 4 due Quiz 2 |

Oct 31 | Optimization primer |

Nov 2 | Geometry of linear classification [HTF 4.5]
Homework 5 due |

Nov 7 | Support vector machines [HTF 12.1, 12.2] |

Nov 9 | Kernels [HTF 12.3]
Homework 6 due Quiz 3 |

Nov 14 | Kernels, cont'd
Multiclass classification |

Nov 16 | Decision trees [HTF 9.2]
Homework 7 due |

Nov 21 | Boosting, bagging, and random forests [HTF 10.1, 10.2, 15.1, 15.2] |

Nov 23 | No class: Thanksgiving |

Nov 28 | Clustering [HTF 14.3]
Homework 8 due Quiz 4 |

Nov 30 | Informative projections [HTF 14.5] |

Dec 5 | Deep learning [HTF 11] |

Dec 7 | Deep learning
Homework 9 due Quiz 5 |