CSE 515 Statistical Methods in Computer Science Instructor: Pedro Domingos

Logistics Instructor: Pedro Domingos Email: pedrod@cs.washington.edu Office: 648 Allen Center Office hours: Tuesdays 11:00am-10:50am TA: Yao Lu Email: luyao@cs.washington.edu Office: 220 Allen Center Office hours: Thursdays 11:00am-11:50am Web: www.cs.washington.edu/515

Evaluation Four homeworks (25% each) Handed out after weeks 2, 4, 6 and 8 Due two weeks later Include programming

Textbook D. Koller & N. Friedman, Probabilistic Graphical Models: Principles and Techniques, MIT Press. Complements: S. Russell & P. Norvig, Artificial Intelligence: A Modern Approach (3rd ed.), Prentice Hall, 2010. M. DeGroot & M. Schervish, Probability and Statistics (3rd ed.), Addison-Wesley, 2002. Papers, etc.

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

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

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

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

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

Relations to Other Classes CSE 546/547: Machine Learning CSE 573: Artificial Intelligence Application classes (e.g., Comp Bio) Statistics classes EE classes

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

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

CSE 515 in One Slide We will learn to: Put probability distributions on everything Learn them from data Do inference with them

Topics (I) Basics of probability and statistical estimation Mixture models and the EM algorithm Hidden Markov models and Kalman filters Bayesian networks and Markov networks Exact inference Approximate inference

Topics (II) Parameter estimation Structure learning Discriminative learning Maximum entropy estimation Dynamic Bayes nets and particle filtering Relational models Decision theory and Markov decision processes