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CSE 574: Artificial Intelligence II Statistical Relational Learning

CSE 574: Artificial Intelligence II Statistical Relational Learning. Instructor: Pedro Domingos. Logistics. Instructor: Pedro Domingos Email: pedrod@cs.washington.edu Office hours: Wednesdays 3:00-3:50, CSE 648 TA: Aniruddh Nath Email: nath@cs.washington.edu

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CSE 574: Artificial Intelligence II Statistical Relational Learning

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  1. CSE 574: Artificial Intelligence IIStatistical Relational Learning Instructor: Pedro Domingos

  2. Logistics • Instructor: Pedro Domingos • Email: pedrod@cs.washington.edu • Office hours: Wednesdays 3:00-3:50, CSE 648 • TA: Aniruddh Nath • Email: nath@cs.washington.edu • Office hours: Mondays 3:00-3:50, CSE 216 • Web: http://www.cs.washington.edu/574 • Mailing list: cse574a_au11@uw.edu

  3. Source Materials • Textbook:P. Domingos & D. Lowd,Markov Logic: An Interface Layer for AI, Morgan & Claypool, 2009 • Papers • Software:Alchemy, etc. • Models, datasets, etc.:Alchemy Web site (alchemy.cs.washington.edu)

  4. Evaluation • Seminar (Pass/Fail) • Project (100% of grade) • Proposals due: October 19 • Progress report due: November 16 • Presentation in class • Final report due: December 7 • Conference submission: Winter 2012 (!)

  5. Possible Projects • Apply SRL to problem you’re interested in • Develop new SRL algorithm • Other

  6. What Is StatisticalRelational Learning? • A unified approach to AI/ML • Combines first-order logic and probabilistic models • Example: Markov logic • Syntax: Weighted first-order formulas • Semantics: Templates for Markov nets • Inference: Logical and probabilistic • Learning: Statistical and ILP

  7. Why Take this Class? • Powerful set of conceptual tools • New way to look at AI/ML • Powerful set of software tools* • Increase your productivity • Attempt more ambitious applications • Powerful platform for developing new learning and inference algorithms • Many fascinating research problems * Caveat: Not mature!

  8. Information extraction Entity resolution Link prediction Collective classification Web mining Natural language processing Computational biology Social network analysis Robot mapping Activity recognition Personal assistants Probabilistic KBs Etc. Sample Applications

  9. Overview of the Class • Background • Representation • Inference • Learning • Extensions • Applications • Your projects

  10. Background • Markov networks • Representation • Inference • Learning • First-order logic • Representation • Inference • Learning (a.k.a. inductive logic programming)

  11. Representation • “Alphabet soup” • Markov logic • Properties • Relation to first-order logicand statistical models

  12. Inference • Basic MAP and conditional inference • The MC-SAT algorithm • Knowledge-based model construction • Lazy inference • Lifted belief propagation • Probabilistic theorem proving

  13. Learning • Weight learning • Generative • Discriminative • Incomplete data • Structure learning and theory revision • Statistical predicate invention • Transfer learning

  14. Extensions • Continuous domains • Infinite domains • Recursive MLNs • Relational decision theory

  15. Applications (Sampled according to your interests)

  16. Your Projects (TBA)

  17. Class begins here.

  18. AI: The First 100 Years IQ Human Intelligence Artificial Intelligence 1956 2006 2056

  19. AI: The First 100 Years IQ Human Intelligence Artificial Intelligence 1956 2006 2056

  20. AI: The First 100 Years Artificial Intelligence IQ Human Intelligence 1956 2006 2056

  21. The Interface Layer Applications Interface Layer Infrastructure

  22. Networking WWW Email Applications Internet Interface Layer Protocols Infrastructure Routers

  23. Databases ERP CRM Applications OLTP Interface Layer Relational Model Transaction Management Infrastructure Query Optimization

  24. Programming Systems Programming Applications Interface Layer High-Level Languages Compilers Code Optimizers Infrastructure

  25. Hardware Computer-Aided Chip Design Applications Interface Layer VLSI Design Infrastructure VLSI modules

  26. Architecture Operating Systems Applications Compilers Interface Layer Microprocessors ALUs Infrastructure Buses

  27. Operating Systems Applications Software Interface Layer Virtual Machines Infrastructure Hardware

  28. Human-Computer Interaction Applications Productivity Suites Interface Layer Graphical User Interfaces Infrastructure Widget Toolkits

  29. Artificial Intelligence Planning Robotics Applications NLP Multi-Agent Systems Vision Interface Layer Representation Inference Infrastructure Learning

  30. Artificial Intelligence Planning Robotics Applications NLP Multi-Agent Systems Vision Interface Layer First-Order Logic? Representation Inference Infrastructure Learning

  31. Artificial Intelligence Planning Robotics Applications NLP Multi-Agent Systems Vision Interface Layer Graphical Models? Representation Inference Infrastructure Learning

  32. Logical and Statistical AI

  33. We Need to Unify the Two The real world is complex and uncertain Logic handles complexity Probability handles uncertainty

  34. Artificial Intelligence Planning Robotics Applications NLP Multi-Agent Systems Vision Interface Layer Statistical Relational Learning Representation Inference Infrastructure Learning

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