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Instructor: Pedro Domingos
Office hours: Wednesdays 3:003:50 p.m., CSE 648 
TA: Aniruddh Nath Office hours: Mondays 3:003:50 p.m., CSE 216 
Class meets:
Mondays, Wednesdays 1:302:50 p.m. in CSE 203
Date  Topics & Slides  Readings  Project 

Sept. 28  Introduction  Chapter 1   
Oct. 3  Markov networks  Section 2.2   
Oct. 5  Firstorder 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) CoarsetoFine Inference and Learning for FirstOrder 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 MultiAgent Activities from GPS Data (Kathleen) Probabilistic Event Logic for IntervalBased 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 SumProduct Networks (Rob & Igor) CoarsetoFine Variational Inference (Chloé) Unifying Automated Software Bug Localization with Markov Logics (Sai & Congle) Kinectbased Activity Recognition Using Markov Logic Networks (Kevin & Jinna) Hierarchical Clustering Using Markov Logic Networks (Abe) 
   
Dec. 14      Final reports due 
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.


Department of Computer Science & Engineering University of Washington Box 352350 Seattle, WA 981952350 (206) 5431695 voice, (206) 5432969 FAX 