1. CIS 410/510 Probabilistic Methods for Artificial Intelligence Instructor: Daniel Lowd

2. Logistics Instructor: Daniel Lowd Email: lowd@cs.uoregon.edu Office: 239 Deschutes Office hours: TBD Web: http://www.cs.uoregon.edu/classes/10W/cis410pm/

3. Evaluation Four homeworks (15% each) – Handed out on weeks 1, 3, 5 and 7 – Due two weeks later – Include programming Final (40%)

4. Textbook D. Koller & N. Friedman, Probabilistic Graphical Models: Principles and Techniques, MIT Press. Complements: – 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. – Papers, etc.

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

10. Applications Machine learning Data mining Automated reasoning and planning Vision and graphics Robotics Natural language processing and speech Information retrieval Databases and data management Networks and systems Ubiquitous computing Human-computer interaction Simulation Computational biology Computational neuroscience Etc.

11. Section 1: Representation Review of probability Bayesian networks Markov networks Briefly: – Hidden Markov models – Dynamic Bayesian networks – Log linear models How do we represent problems as probability distributions and encode them efficiently when there are many variables?

12. Section 2: Inference Why inference is hard Exact inference – Enumeration (brute force!) – Variable elimination Approximate inference – Loopy belief propagation – Sampling methods How do we use these probability distributions to answer questions efficiently?

13. Section 3: Learning The basics of statistical estimation Learning Bayesian networks Learning Markov networks How can we use data to automatically pick the model’s structure and parameters?

14. Relations to Other Classes CIS 410/510: Machine Learning CIS 410/510: Data Mining CIS 471/571: Artificial Intelligence Application classes (e.g., Comp Bio) Statistics classes

15. 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