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Transcript

CSE 515 Statistical Methods in Computer Science Instructor: Pedro Domingos Logistics • Instructor: Pedro Domingos Email: [email protected] Office: 648 Allen Center Office hours: Wednesdays 11:00am-11:50am • TA: Yuyin Sun Email: [email protected] Office: 218 Allen Center Office hours: Mondays 11:00am-11:50am • Web: www.cs.washington.edu/515 • Mailing list: [email protected] Evaluation • Four homeworks (25% each) – Handed out on 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