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

1 | Introduction, linear classification, perceptron update rule (PDF) |

2 | Perceptron convergence, generalization (PDF) |

3 | Maximum margin classification (PDF) |

4 | Classification errors, regularization, logistic regression (PDF) |

5 | Linear regression, estimator bias and variance, active learning (PDF) |

6 | Active learning (cont.), non-linear predictions, kernals (PDF) |

7 | Kernal regression, kernels (PDF) |

8 | Support vector machine (SVM) and kernels, kernel optimization (PDF) |

9 | Model selection (PDF) |

10 | Model selection criteria (PDF) |

11 | Description length, feature selection (PDF) |

12 | Combining classifiers, boosting (PDF) |

13 | Boosting, margin, and complexity (PDF) |

14 | Margin and generalization, mixture models (PDF) |

15 | Mixtures and the expectation maximization (EM) algorithm (PDF) |

16 | EM, regularization, clustering (PDF) |

17 | Clustering (PDF) |

18 | Spectral clustering, Markov models (PDF) |

19 | Hidden Markov models (HMMs) (PDF) |

20 | HMMs (cont.) (PDF) |

21 | Bayesian networks (PDF) |

22 | Learning Bayesian networks (PDF) |

23 |
Probabilistic inference Guest lecture on collaborative filtering (PDF) |

24 | Current problems in machine learning, wrap up |

## Lecture Notes

Course Info

Instructors

Departments

As Taught In

Fall
2006

Level

Topics

Learning Resource Types

*assignment_turned_in*Problem Sets with Solutions

*grading*Exams with Solutions

*notes*Lecture Notes