The readings in the right-hand column are either lecture notes or sections in Grinstead and Snell (GS).

Date |
Topic |
Reading |

Sep 27 | Introduction | Mathematical preliminaries, GS 1.2 |

Sep 29 | Probability spaces | Probability spaces, GS 3.1, 3.2 |

Oct 4 | Multiple events | |

Oct 6 | Conditional probability | Multiple events, conditioning, and independence I, GS 4.1 |

Oct 11 | Conditional probability, Independence | Multiple events, conditioning, and independence II |

Oct 13 | Random variables | Random variables, expectation, and variance I |

Oct 18 | Linearity of expectation | GS 6.1, 6.2 |

Oct 20 | Variance | Random variables, expectation, and variance II |

Oct 25 | Randomized algorithms: sorting and selection | Randomized algorithms I |

Oct 27 | Midterm 1 | |

Nov 1 | Clustering and graph cuts | |

Nov 3 | Hashing | Randomized algorithms II |

Nov 8 | Information retrieval | Randomized algorithms III |

Nov 10 | Random generation | Random generation I |

Nov 15 | The binomial in sampling and hypothesis testing | Random generation II |

Nov 17 | Midterm 2 | Useful facts for the midterm |

Nov 22 | The central limit theorem | |

Nov 24 | Sampling and hypothesis testing, cont'd | Sampling, hypothesis testing, and the central limit theorem |

Nov 29 | Nearest neighbor classification and decision trees | |

Dec 1 | Linear classifiers | Machine learning |