These lecture notes occasionally refer to slides, such as at the end of lectures 5 and 7. These slides are not available on MIT OpenCourseWare.

LEC # | TOPICS |
---|---|

1 | Rule mining and the Apriori algorithm (PDF) |

2 | R for machine learning (PDF) (Courtesy of Allison Chang. Used with permission.) |

3 | Fundamentals of learning (PDF) |

4 | Inference (PDF) |

5 | Clustering (PDF) |

6 | k-nearest neighbors (PDF) |

7 | Naïve Bayes (PDF) |

8 | Decision trees (PDF) |

9 | Logistic regression (PDF) |

10 | Boosting (PDF) |

11 | Convex optimization (PDF) |

12 | Support vector machines (PDF) |

13 | Kernels (PDF) |

14 | Statistical learning theory (PDF) |

15 | Bayesian analysis (PDF - 1.2MB) (Courtesy of Ben Letham. Used with permission.) |