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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?
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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
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CSE 546/547: Machine Learning
CSE 573: Artificial Intelligence
Application classes (e.g., Comp Bio)
Statistics classes
EE classes
Applications in CS (I)
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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)
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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)
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Parameter estimation
Structure learning
Discriminative learning
Maximum entropy estimation
Dynamic Bayes nets and particle filtering
Relational models
Decision theory and Markov decision
processes