## CIS 520 - Fall 08 |

Date | Subject | Problem Set | Reading | Notes |
---|---|---|---|---|

Sep 3 | Intro; Point Estimation | Bishop 2.1, Appendix B | ||

Sep 8 | Matlab Tutorial by Ben Sapp | |||

Sep 10 | Gaussians, Linear models, Regression | PS 1 out on Friday in BlackBoard | Bishop 1.1 to 1.4, Bishop 3.1, 3.1.1, 3.1.4, 3.1.5, 3.2, 3.3, 3.3.1, 3.3.2 |
Least Squares Regression Applet |

Sep 15 | Bias-Variance Decomposition, Naive Bayes and Logistic Regression | Bishop 1.3, 1.5, 3.2; Mitchell's Chapter on Naive Bayes and Logistic | Classification Applet | |

Sep 17 | Generative vs Discriminative, Naive Bayes and Logistic Regression | Bishop 4.0, 4.2, 4.3, 4.4, 4.5; Mitchell's Chapter on Naive Bayes and Logistic | ||

Sep 22 | Logistic Regression Continued, Decision Trees | Bishop 4.0, 4.2, 4.3, 4.4, 4.5; Bishop 1.6 (Information Theory); Bishop 14.4 (Tree Models) | Decision trees Applet | |

Sep 24 | Decision Trees | Bishop 1.6 (Information Theory); Bishop 14.4 (Tree Models), Nilsson's Chapter | ||

Sep 29 | Decision Trees and Boosting | PS 1 Due at 5pm | Bishop 14.3 (Boosting); Schapire's Boosting Tutorial | |

Oct 1 | Boosting, Regularization, Cross-validation, Model Selection | PS2 out on Thursday | Bishop 1.3, 3.1.4; | |

Oct 6 | Cross-validation, Model Selection; Neural Networks | Bishop 3.1.4; Bishop 5.1 | Optional Reading: Ron Kohavi's paper, A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection. | |

Oct 8 | Neural Networks; Nonparametric Methods | Bishop 5.1-5.3 (NNs); Bishop 2.5 Nonparametric Methods | ||

Oct 13 | Happy Fall Break | |||

Oct 15 | Nonparametric Methods, Nearest Neighbors | Bishop 2.5 Nonparametric Methods | ||

Oct 20 | Midterm |
Previous Midterms from CMU ML class |
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Oct 22 | Kernel Methods, Support Vector Machines |
(Bishop 6.1,6.2) Kernels (Bishop 7.1) Maximum Margin Classifiers Additional Material: Hearst 1998: High Level Presentation Burges 1998: Detailed Tutorial |
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Oct 27 | Kernel Methods, Support Vector Machines cont. |
(Bishop 6.1,6.2) Kernels (Bishop 7.1) Maximum Margin Classifiers |
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Oct 29 | Kernel cont., Generalization Bounds |
(Bishop 6.1,6.2) Kernels (Bishop 7.1) Maximum Margin Classifiers |
LIBSVM Applet | |

Nov 3 | Generalization Bounds, PAC Learning |
Goldman's
COLT survey, sections 1-3.1 Avrim Blum's course handout on tail inequalities |
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Nov 5 | Generalization Bounds, PAC Learning | PS 3, Project Out | ||

Nov 10 | Project Review | |||

Nov 12 | Bayes nets - Representation | (Bishop 8.1,8.2) Bayesian Networks | ||

Nov 17 | Bayes nets - Inference |
(Bishop 8.4.1,8.4.2) - Inference in Chain/Tree Structures Rabiner's HMM Tutorial |
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Nov 19 | Bayes nets - Inference, cont. | |||

Nov 24 | Unsupervised Learning, Clustering | PS 4 out | (Bishop 9.1, 9.2) - K-means, Mixtures of Gaussians | |

Nov 26 | No class -- Happy Thanksgiving! | |||

Dec 1 | K-Means, Clustering | (Bishop 9.1, 9.2) - K-means, Mixtures of Gaussians | ||

Dec 3 | EM |
(Bishop 9.3, 9.4) - EM Neal and Hinton EM paper |
EM: Mixture of Gaussians Applet | |

Dec 12 | Final: 12-3pm, Wu & Chen | Practice Exams: from CMU ML class |