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

2. Logistics Instructor: Pedro Domingos Email: pedrod@cs.washington.edu Office: 648 Allen Center Office hours: Wednesdays 3:00-3:50 TA: Matt Richardson Email: mattr@cs.washington.edu Office: TBA Office 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