Due date | Topic | Readings | Assignment | Additional files | Slides |
---|---|---|---|---|---|

1/17 | Overview | ML 2 (assumed background) | Piazza, ipython notebook | slides | |

1/19 | Learning | ISLR 2, ML 1 | ISLR 2 | slides | |

1/24 | Probability, Cross Validation | ML 2 | distributions | slides (some math missing) slides (black background) | |

1/26 | Probability Lab | lab 1 | |||

1/31 | Bayesian Concept learning | ML 3-3.3 | slides | ||

2/2 | Naive Bayes | ML 3.5, 5 | Lab 1 | hw 1 solutions [pdf] [tex] | slides
audio |

2/7 | LDA/QDA | ML 4.1-4.2 | slides | ||

2/9 | Lab 2 NB, KNN | Hw 1 (due Sunday 5pm) | lab 2
lab2 eval HW 2 solutions [pdf] [tex] | ||

2/14 | Linear model | ML 7-7.3 | slides | ||

2/16 | Regularization | ML 7.4-7.5 | Lab 2 | slides | |

2/21 | Logistic Regression | ML 8-8.3 | Kaggle | slides | |

2/23 | Lab 3 Linear/Logistic Regression | Hw 2 | lab 3 | ||

2/28 | Logistic Reg | ML 8.5-8.6.2 | slides | ||

3/2 | General Linear Models | ML 9.3-9.4.2, 9.6-9.7 | Lab 3 | slides | |

3/7 | Neural Networks | ML 16.5-16.5.6 | NN slides
BackProp slides |
||

3/9 | Lab 4 NN | delta learning
back prop |
lab 4 | ||

3/14 | Test review | ||||

3/16 | Test | solutions | |||

3/26 | Due dates |
Kaggle
Lab 4 |
|||

3/28 | Gaussian Mixture | ML 11-11.4 | slides | ||

3/30 | Expectation Maximization | ML 11.4-11.6 | slides | ||

4/4 | PCA | ML 12 | slides | ||

4/6 | Lab 5 | Lab 5 | |||

4/11 | SVM | 14-14.4 | Kaggle | slides | |

4/13 | Kernel Methods | 14.5, SVM tutorial | Lab 5 | hw 4
[tex] |
slides |

4/18 | SVM | 14.5, SVM tutorial | |||

4/20 | Lab 6 SVM | Lab 6 | |||

4/25 | Clustering | ML 25 | Hw 4 | ||

4/27 | Reinforcement learning | ML 16.4-16.6 | Lab 6 | slides | |

5/2 | Boosting | slides | |||

5/4 | test review | Kaggle | |||

5/9 | Exam 1:30-4 |