Steam-powered Turing Machine University of Washington Department of Computer Science & Engineering
 CSE 574 - Artificial Intelligence II - Autumn 2011
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Statistical Relational Learning

Instructor: Pedro Domingos
Office hours: Wednesdays 3:00-3:50 p.m., CSE 648
TA: Aniruddh Nath
Office hours: Mondays 3:00-3:50 p.m., CSE 216

Class meets:
Mondays, Wednesdays 1:30-2:50 p.m. in CSE 203


Date Topics & Slides Readings Project
Sept. 28 Introduction Chapter 1 -
Oct. 3 Markov networks Section 2.2 -
Oct. 5 First-order logic and inductive logic programming Section 2.1 -
Oct. 10 Markov logic and other SRL approaches Sections 2.3 and 2.4 -
Oct. 12 Markov logic (contd.) Sections 2.3 and 2.4 -
Oct. 17 Applications of Markov logic Chapter 6, Alchemy tutorial -
Oct. 19 Applications of Markov logic (contd.) Chapter 6, Alchemy tutorial Proposals due
Oct. 24 Applications of Markov logic (contd.) Chapter 6, Alchemy tutorial -
Oct. 26 Applications of Markov logic (contd.) Chapter 6, Alchemy tutorial -
Oct. 31 Inference Chapter 3 -
Nov. 2 Inference (contd.) Chapter 3 -
Nov. 7 Weight learning Section 4.1 -
Nov. 9 Structure learning Sections 4.2, 4.3 and 4.4 -
Nov. 14 Probabilistic Theorem Proving (Chloé)
Coarse-to-Fine Inference and Learning for First-Order Probabilistic Models (Austin)
- -
Nov. 16 Tuffy: Scaling up Statistical Inference in Markov Logic Networks Using an RDBMS (Emad)
Scaling Textual Inference to the Web (Cullen)
Approximate Inference for Planning in Stochastic Relational Worlds (Igor)
- Progress reports due
Nov. 21 Hybrid Markov Logic Networks (Abe)
Recognizing Multi-Agent Activities from GPS Data (Kathleen)
Probabilistic Event Logic for Interval-Based Event Recognition (Kevin)
- -
Nov. 23 Event Modeling and Recognition Using Markov Logic Networks (Jinna)
Collective Semantic Role Labelling with Markov Logic (To)
Automatically Refining the Wikipedia Infobox Ontology (Sai)
- -
Nov. 28 Relational Reinforcement Learning (Svet)
Policy Transfer via Markov Logic Networks (Yanping)
Modelling Relational Data Using Bayesian Clustered Tensor Factorization (Rob)
- -
Nov. 30 Joint Unsupervised Coreference Resolution with Markov Logic (Congle)
Unsupervised Semantic Parsing (Conrad)
- -
Dec. 5 Semantic Role Labeling for English Using Markov Logic (To)
Scale up Data Curation with MLN approach (Emad)
Information Extraction from US Bankruptcy Petition Forms (Yanping)
Markov Logic for Inverse Reinforcement Learning (Svet)
Picard (Kathleen)
- -
Dec. 7 Modelling Character Sequences with Sum-Product Networks (Rob & Igor)
Coarse-to-Fine Variational Inference (Chloé)
Unifying Automated Software Bug Localization with Markov Logics (Sai & Congle)
Kinect-based Activity Recognition Using Markov Logic Networks (Kevin & Jinna)
Hierarchical Clustering Using Markov Logic Networks (Abe)
- -
Dec. 14 - - Final reports due


Pedro Domingos and Daniel Lowd, Markov Logic: An Interface Layer for Artificial Intelligence, Morgan & Claypool, 2009.
(Available free for download from the link above, for UW IP addresses.)


Students will do a project and give a seminar. Projects can be done individually or in groups of two, and are due on December 14. 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, or developing a new SRL algorithm. You can also propose any other project related to SRL.

Suggested papers for seminar.


You can use Alchemy or any other SRL system for your project, or roll your own. See related links on the Alchemy Web site.
Send questions, bug reports, etc., on Alchemy and the Alchemy Web site to (or post to the class mailing list below, if of general interest).

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