Electrical Engineering and Computer Science Department

Northwestern University

**Instructor:** Doug Downey

**Office Hours:** 1:00-2:00PM Monday (or by appt), Ford 3-345

**Email:** ddowney <at> eecs <dot> northwestern <dot> edu

**Teaching Assistants:** Chandra Sekhar Bhagavatula,
Shengxin Zha and
Kathy Lee

**Office Hours:** Thursday 2PM-3PM, Ford 3-211 and Friday, 3PM-5PM, Tech L440

**Email:** chandrabhagavatula2011 <at> u <dot> northwestern <dot> edu,
shengxinzha2011 <at> u <dot> northwestern <dot> edu,
kathy <dot> lee <at> eecs <dot> northwestern <dot> edu

Homework will be submitted via Blackboard. Details on the specific files to include are given in each homework assignment.

Late assignments are penalized by 5% a day, and will NOT BE ACCEPTED more than one week after the original deadline.

Problem Set 1 | Due 11:59PM Tuesday, Jan 21 | 10 pts |

Problem Set 2 | Due 11:59PM Tuesday, Jan 28 | 20 pts |

Problem Set 3 | Due 11:59PM Tuesday, Feb 18 | 10 pts |

Problem Set 4 | Due 11:59PM Thursday, March 13 | 10 pts |

Deadlines:

Proposal (1 pg) | Due 11:59PM Thursday, Feb 6 | 10 pts |

Status Report (1-2 pg) | Due 11:59PM Tuesday, March 4 | 10 pts |

Project Video | Due 9AM Friday, March 21 | 20 pts |

Project Web page (details in link above) | Due 9AM Friday, March 21 | 15 pts |

Week of January 6 |
Wired data-mining article Forbes article on ML popularity Alpaydin Ch. 1,2; Mitchell Ch. 1,2 |

Week of Jan 13 |
Alpaydin Ch. 8,9; Mitchell Ch. 3,8 |

Week of Jan 20 |
None |

Week of Jan 27 |
Alpaydin Ch. 10.6; Mitchell Ch. 9 |

Week of Feb 3 |
Alpaydin Ch. 11; Mitchell Ch. 4 |

Week of Feb 10 |
Alpaydin Ch. 4.2, 16; Mitchell Ch. 6 Recommended: Andrew Moore tutorial on Bayes Nets |

Week of Feb 17 |
Alpaydin Ch. 10 Optional: Modeling Redundancy in Web Information Extraction |

Week of Feb 24 |
Alpaydin Ch. 7.4, 15; Mitchell Ch. 7 |

Week of Mar 3 |
Alpaydin Ch. 13 Recommended: SVM Tutorial |

Week of Jan 6 |
M: No class (NU closed for weather) W: Introduction F: Decision Trees |

Week of Jan 13 | M-W: Decision Trees (cont.) F: Instance-based Learning |

Week of Jan 20 | M: No class (MLK) W: Instance-based Learning (cont) F: Distance Measures |

Week of Jan 27 |
M: Project Guidelines and Suggestions W: Greedy Local Search, Optimization F: Genetic Algorithms |

Week of Feb 3 | M: Neural Networks W: Neural networks (cont.) F: Neural networks (cont.), Basics of Probability for Machine Learning |

Week of Feb 10 | M: Basics of Prob. (cont.) W: Statistical Estimation F: Bayes Nets |

Week of Feb 17 |
M: Naive Bayes Classifiers W: Web Information Extraction F: Logistic Regression |

Week of Feb 24 |
M: Hidden Markov Models W: Clustering, EM F: Clustering, EM (cont.) |

Week of March 3 |
M: Computational Learning Theory and Evaluating Hypotheses W: Project Status Reports F: Support Vector Machines |

Week of March 10 |
M: Ensemble Methods W: Reinforcement Learning F: Active Learning |