The following schedule is *teantive and may change* as the course progresses.

Most required readings are taken from the course textbook *Machine Learning: A Probabilistic Perspective* (MLaPP). We use ESLII to refer to the book Elements of Statistical Learning by Hastie, Tibshirani and Friedman, which is freely available.

There are **two** types of slides:

: URLs in the*Lecture slides***Lectures**column point to slides used in the lectures, which contain administravia, reviewed and new topics, and other course-specific information. Material in those slides are required. Slides in this column are often released prior to the corresponding lectures and are updated (once) after the lectures to make adjustment, correct typos etc.: URLs in the*Topic slides***Topics**column, however, are in general free of course-specific details and they may go into more advanced material that relate to the topics. Not all material in those slides are required --- they are suppmenetary to those who want to know a bit more. As such, slides in this column are*work-in-progress*and are updated whenver I have time.

Date | Topics | Lectures | Readings | Notes |
---|---|---|---|---|

8/26 | Overview Nearest neighbor classification |
slides | MLaPP: sec. 1.1, 1.2 1.4.2 | Take-home special quiz |

8/28 | Nearest neighbor classification | slides | MLaPP: sec. 1.1, 1.2, 1.4.2, 2.1 -- 2.5 Recitation slides on basic math |
Recitation: special quiz |

9/2 | No class | Labor day ☺ | ||

9/4 | Linear regression | slides | MLaPP: sec. 1.2, 1.4.1 -- 1.4.5, 4.1 ESLII: sec. 2.3, 3.1, 3.2 -- 3.2.1 |
Recitation:
basic math
Homework 1 is out |

9/9 | Linear regression Logistic regression |
slides | MLaPP: sec 7.1-7.3, 7.5.1-7.5.2 | |

9/11 | Model selection Logistic regression |
slides | MLaPP: 1.4.6 - 1.4.8, 8.1-8.3.4 | Recitation: Convex duality |

9/16 | Logistic regression Gaussian discriminatn analysis Generative vs. Discriminative |
slides | MLaPP: 4.2.1 - 4.2.4, 8.6.1 | Recitation: |

9/18 | Generative vs. discriminative Multinomial logistic regression |
slides | MLaPP: 8.3.7 | Recitation: Homework 2 is out |

9/23 | Nonlinear basis functions Overfitting |
slides | MLaPP: 6.4.4, 6.5, 7.5 | Recitation: |

9/25 | Bias/variance tradeoff | slides | ESLII: sec7.1-7.3 Prof. Kakade's note |
Recitation: |

9/30 | Basic concepts in Bayesian approach | slides | MLaPP: sec 2.4.5, 3.3, 7.6 | Mock Quiz 1 |

10/2 | Bayesian linear regression | slides | MLaPP: sec 2.4.5, 3.3, 7.6 | |

10/7 | Kernel methods | slides | MLaPP: sec 2.4.5, 3.3, 7.6 | Homework 3 |

10/9 | Kernel methods Gaussian Process |
slides | MLaPP: sec 14.1, 14.2 up to 14.2.5, 14.4, 15.1 -- 15.2.4 | |

10/16 | Perceptron SVM |
slides | MLaPP: sec 8.5.4 14.5 | |

10/21 | SVM | slides | MLaPP: sec 8.5.4 14.5 | |

10/23 | Boosting | slides | MLaPP: sec 16.4 (up to 16.4.5), 16.4.8 | Homework 4 |

10/28 | Clustering, GMM | slides | MLaPP: sec 1.3 11.2--11.2.1, 11.4--11.4.2, 25.1 | |

11/4 | EM Dimensionality reduction |
slides
slides slides |
MLaPP: 11.4.3, 12.2.1-12.2.2, 14.4.4 | |

11/11 | Bayes nets | slides | MLaPP: 10.1, 10.2.1 - 10.2.3 | |

11/13, 11/20 | Bayes nets | slides | MLaPP: 10.1, 10.2.1 - 10.2.3, 10.3, 17.1-17.3 Bishop book chapters |
Homework 5 |

11/25 | Markov networks | slides | Bishop book chapters | Homework 6 |