1. CSE 515 Statistical Methods in Computer Science
Instructor:
Pedro Domingos

2. Logistics
Instructor: Pedro Domingos Office: 648 Allen Center Office hours: Tuesdays 11:00am-10:50am
TA: Yao Lu Office: 220 Allen Center Office hours: Thursdays 11:00am-11:50am
Web:

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

4. 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.

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 546/547: Machine Learning
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. CSE 515 in One Slide We will learn to:
Put probability distributions on everything
Learn them from data
Do inference with them

14. 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

15. 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