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CSE 590ST Statistical Methods in Computer Science

This course covers the fundamentals of statistical methods for computer science, including probabilistic models, inference, learning, and applications in various areas of CS.

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CSE 590ST Statistical Methods in Computer Science

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  1. CSE 590STStatistical Methods in Computer Science Instructor: Pedro Domingos

  2. Logistics • Instructor: Pedro DomingosEmail: pedrod@cs.washington.eduOffice: 648 Allen CenterOffice hours: Wednesdays 3:00-3:50 • TA: Matt RichardsonEmail: mattr@cs.washington.eduOffice: TBAOffice hours: Mondays 3:00-3:50 • Web: www.cs.washington.edu/590st • Mailing list: cse590st

  3. Evaluation • Four homeworks (15% each) • Handed out on weeks 2, 4, 6 and 8 • Due two weeks later • Include programming • Final (40%)

  4. Textbooks • D. Koller & N. Friedman, Bayesian Networks and Beyond: Probabilistic Models for Learning and Reasoning, MIT Press. (Handouts.) • S. Russell & P. Norvig, Artificial Intelligence: A Modern Approach (2nd ed.), Prentice Hall, 2003. • M. DeGroot & M. Schervish, Probability and Statistics (3rd ed.), Addison-Wesley, 2002. • Other book chapters and papers.

  5. What Is Probability? • Probability: Calculus for dealing with nondeterminism and uncertainty • Cf. Logic • Probabilistic model: Says how often we expect different things to occur • Cf. Function

  6. What’s in It for Computer Scientists? • Logic is not enough • The world is full of uncertainty and nondeterminism • Computers need to be able to handle it • Probability: New foundation for CS

  7. What Is Statistics? • Statistics 1: Describing data • Statistics 2: Inferring probabilistic models from data • Structure • Parameters

  8. What’s in It for Computer Scientists? • Statistics and CS are both about data • Massive amounts of data around today • Statistics lets us summarize and understand it • Statistics lets data do our work for us

  9. Stats 101 vs. This Class • Stats 101 is a prerequisite for this class • Stats 101 deals with one or two variables; we deal with tens to thousands • Stats 101 focuses on continuous variables; we focus on discrete ones • Stats 101 ignores structure • We focus on computational aspects • We focus on CS applications

  10. Relations to Other Classes • CSE 546: Data Mining • CSE 573: Artificial Intelligence • Application classes (e.g., Comp Bio) • Statistics classes • EE classes

  11. Applications in CS (I) • Machine learning and data mining • Automated reasoning and planning • Vision and graphics • Robotics • Natural language processing and speech • Information retrieval • Databases and data management

  12. Applications in CS (II) • Networks and systems • Ubiquitous computing • Human-computer interaction • Simulation • Computational biology • Computational neuroscience • Etc.

  13. Topics (I) • Review of basics • Bayesian networks • Inference in Bayes nets • Exact inference • Approximate inference • Learning Bayes nets • Maximum likelihood and Bayesian estimation • The EM algorithm • Structure learning

  14. Topics (II) • Mixture models • Markov networks • Sequential models • Hidden Markov models • Kalman filters • Dynamic Bayes nets • Particle filtering

  15. Topics (III) • Relational models • Decision theory and MDPs • Information theory

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