Information | Syllabus | Assignments

*Note: this syllabus is tentative and subject to change. Some
readings are available from from blackboard using your OIT
userID and password.*

Tu 2/5 | Introduction | notes slides | |

Th 2/7 | Probability and statistics review (I) | Freedman,D. (1994). Some issues on the foundation of statistics. (optional) | slides notes (Paranada) notes (Kim) |

Tu 2/12 | Probability and statistics review (II) | notes (Soroka and Tsinis) | |

Th 2/14 | Naive Bayes classification | notes (Ho and Ye) slides | |

Tu 2/19 | Support vector machines | Burges, C. (1998). A tutorial on support vector machines for pattern recognition. (read pp 1-10.) | notes (Lloyd and Terrace) |

Th 2/21 | Kernel methods and boosting | Schapire, R. (2003). The boosting approach to machine learning: An overview. | notes (Tan) |

Tu 2/26 | More boosting | notes (Seidel and DiFiore) slides | |

Th 2/28 | K-means clustering and agglomerative clustering | notes (Pop and Kim) slides | |

Tu 3/4 | Agglomerative clustering (cont) and mixture models | notes (An and Mutungu) slides | |

Th 3/6 | Mixture modeling | notes (Golightly and Prabhu) slides of examples | |

Tu 3/11 | Expectation maximization | notes (Luo) notes (Mackowski) | |

Th 3/13 | Hidden Markov models | Bishop Ch 13 (on blackboard, under e-reserves) | notes (Yun-En and Ashwin) |

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Tu 3/25 | Hidden Markov models (II) | notes (Savir and Simon) | |

Th 3/27 | Hidden Markov models (III) | notes (Wolf and Hsu) | |

Tu 4/1 | Linear regression | Hastie et al. (41-65) Hastie et al. (115-120) Bishop (137-152) | notes (Chen and Huang) slides |

Th 4/3 | Linear regression (II) | notes (Lee) notes (DeCoro) See slides above | |

Tu 4/8 | Linear regression (III) | notes (Herbach and Gorman) See slides above | |

Th 4/10 | Logistic regression | Ch 13 from Wasserman's "All of Statistics" (The reading is under "Course Materials." Focus on the section on logistic regression.) | notes (DiMaggio) notes (Hung) |

Tu 4/15 | Generalized linear models | McCullagh and Nelder, Chapter 2 | notes (Polatkan) |

Th 4/17 | Applications : Computer vision (Guest: Prof. Fei-Fei Li) | Optional readings: Fei-Fei et al. (2006) Fei-Fei and Perona (2005) Viola and Jones Blei et al. (2003) | |

Tu 4/22 | Applications : Neuroscience (Guest: Prof. Kenneth Norman) | ||

Th 4/24 | Principal components analysis | Hastie et al. (485--502) | notes (Bell and Pop) |

Tu 4/29 | Factor analysis | ||

Th 5/1 | Topic models (and class summary) |