CSE 515 Statistical Methods in Computer Science

1
CSE 515Statistical Methods in Computer Science

• Instructor
• Pedro Domingos

2
Logistics

• Instructor Pedro DomingosEmail
  pedrod_at_cs.washington.eduOffice 648 Allen
  CenterOffice hours Wednesdays 330-420
• TA Daniel LowdEmail lowd_at_cs.washington.eduOffi
  ce 216 Allen CenterOffice hours Mondays
  300-350
• Web www.cs.washington.edu/515
• Mailing list cse515

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,Structured Probabilistic
  ModelsPrinciples and Techniques, MIT Press.
• Complements
• S. Russell P. Norvig, Artificial
  IntelligenceA 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
Whats 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
Whats 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 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