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CSE 574 - Artificial Intelligence II - Spring 2005 |
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Instructor: Pedro Domingos
Office: CSE 648 Office hours: Wednesdays 2:00-2:50 p.m., and by appointment |
TA: Stanley Kok Office: CSE 216 Office hours: Mondays 4:30-5:30 p.m., and by appointment |
Class meets:
Mondays, Wednesdays 3:00-4:20 p.m. in
EE1 026
| Week | Topics & Lecture Notes | Readings | Milestones |
|---|---|---|---|
| Week 1 (Mar 28 & 30) | Introduction | ||
| Week 2 (Apr 4 & 6) | Inductive logic programming | Dzeroski tutorial |
Project proposal due (Fri, Apr 8) |
| Week 3 (Apr 11 & 13) | Bayesian networks | Heckerman tutorial | |
| Week 4 (Apr 18 & 20) | Markov networks | Della Pietra et al. | |
| Week 5 (Apr 25 & 27) | Markov logic networks | Richardson & Domingos | |
| Week 6 (May 2 & 4) | MLN structure learning Probabilistic relational models (Guest lecture by Lise Getoor) |
Kok & Domingos Getoor et al. (1) Getoor et al. (2) |
Project progress report due (Fri, May 6) |
| Week 7 (May 9) |
Discriminative training of MLNs (Parag
Singla) Relational stochastic processes (Sumit Sanghai) |
Singla
& Domingos Sanghai et al. |
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| Week 7 (May 11) |
Lifted inference (Daniel Lowd) SRL for information extraction and NLP (Michele Banko) |
de Salvo Braz
et al.
Emailed to class |
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| Week 8 (May 16) |
Feature generation and selection in SRL (Stef
Schoenmackers) Aggregation (Pat Tressel) |
Emailed to class Perlich & Provost |
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| Week 8 (May 18) |
Stochastic logic programs (Kevin
Wampler) Probabilistic models with unknown objects (Brian Ferris) |
Muggleton Emailed to class |
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| Week 9 (May 23) |
Conditional random fields (Benson
Limketkai) Relational Markov networks (Danny Wyatt) |
Lafferty et al. Taskar et al. |
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| Week 9 (May 25) |
Relational factor graphs (Lin Liao) Max-margin Markov networks (Mike Cafarella) |
Emailed to class Taskar et al. |
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| Week 10 (Jun 1) |
1. MLNs for table induction from Web text (Michele Banko & Mike
Cafarella) |
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| Week 11 (Jun 9, 2:30 pm) | 1. Activity recognition using aggregate features
(Lin Liao) 2. Relational mixture models (Daniel Lowd) 3. Semi-supervised and active relational learning (Sumit Sanghai) 4. Efficient inference for MLNs (Parag Singla) 5. Incorporating continuous random variables into MLNs (Pat Tressel) 6. Inferring social roles and interaction types using MLNs (Danny Wyatt) |
Project report due (Mon, Jun 6, midnight) |
Students will do a project and give a seminar. Projects can be done individually or in groups of two, and are due on the last day of the last week of classes (June 3). Seminars are done individually, and are Pass/Fail; a Pass is required to complete the class. The class grade will be the project grade. An ideal project is applying statistical relational learning (SRL) to your area of research. You can also propose any other project related to SRL. Here are some ideas:
We will provide software for statistical relational learning based on Markov logic networks. Watch this space ...
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Department of Computer Science & Engineering University of Washington Box 352350 Seattle, WA 98195-2350 (206) 543-1695 voice, (206) 543-2969 FAX [comments to koks@cs.washington.edu] |