9/5 |
Overview |
CML 1.1-1.2; PC 1.1-1.6. |

9/5 |
Nearest neighbor classifiers |
CML 3.1-3.3; PC 4.5.5, 4.6. |

9/10 |
Predictions |
CML 9.1-9.2; PC 3.2; coin tosses handout. |

9/12, 9/17 |
Generative models |
CML 9.3-9.5; PC 2.4-2.6, 4.4. |

9/19 |
Risk estimation, model selection/averaging |
CML 5.6; optional: prediction theory tutorial. |

9/24, 9/26 |
Linear regression |
CO 3.1.1, 3.1.3-3.1.5, 3.1.8-3.1.9, 3.2.1-3.2.4; linear regression handout. |

10/1, 10/3 |
Logistic regression and linear classifiers |
CML 4.1-4.7; 5.1, 5.3-5.4; PC 5.1-5.3; Perceptron handout; online-to-batch handout. |

10/8, 10/10 |
Support vector machines |
CML 7.7, 11.1-11.2, 11.4-11.6. |

10/22, 10/24, 10/29 |
Generalization theory |
CML 12.1-12.4; optional: statistical learning theory tutorial. |

10/29 |
Convex optimization |
CML 7.1-7.3; CO 2.1.1-2.1.4, 2.2-2.3.2, 4.2.1-4.2.3. |

10/31 |
Optimization algorithms |
CML 7.4, 14.2; PC 5.4; CO 9.2-9.3; stochastic gradient tricks; gradient descent demo, stochastic gradient method demo. |

11/7 |
Neural networks |
CML 10.1-10.5; PC 6.1-6.3; efficient backprop; multi-layer networks handout. |

11/12 |
No lecture |
PyTorch 60 Minute Blitz tutorial |

11/14 |
Classification objectives |
CML 5.5, 6.1-6.2, 8.1; one-against-all handout. |

11/19 |
Ensemble methods |
BFA 1.1-1.3, 3.4.3; CML 1.3-1.6, 13.1-13.2; PC 8.3, 9.4.2, 9.5.1-9.5.2; AdaBoost handout. |

11/26 |
Clustering |
CML 3.4-3.5, 15.1; PC 10.9. |

11/28 |
Societal consequences |
CML 8.4; how big data is unfair; ProPublica article on COMPAS. |

12/3 |
Principal components analysis |
CML 15.2; notes on PCA 5.1, 5.2, 5.4. |