CSE 250A. Principles of Artificial Intelligence:

Administration Content Prerequisites Textbooks Syllabus Gradesource Lectures 
There will be a quiz in class every Tuesday starting on
October 9, 2012. The last two quizzes will be on
Thursdays, on November 29 and December 6. In total there
will be 9 quizzes. The lowest quiz grade will be
discarded, so one quiz may be missed without penalty.
There will be eight homework assignments. The first six
will be handed out on Thursdays, starting on October 4,
and due back at the start of lecture the following
Thursday. Quizzes and assignments will both be done in
pairs; please pay attention to detailed instructions.
Please ask questions using Piazza.
Methods based on probability theory for reasoning and learning under uncertainty. Topics will include directed and undirected probabilistic graphical models, exact and approximate inference, latent variables, expectationmaximization, hidden Markov models, Markov decision processes, applications to vision, robotics, speech, and/or text.
The course is aimed primarily at firstyear graduate students in mathematics, science, and engineering. Prerequisites are elementary probability, multivariable calculus, linear algebra, and basic programming ability in a highlevel language such as C, Java, R, or Matlab. Programming assignments are completed in the language of the student's choice.
CSE 150
covers some of the same material as 250A, but at a slower
pace and less advanced mathematical level. The homework
assignments in CSE 250A are longer and more challenging.
CSE 250B
is at the same level as 250A, but has different content
and style. Students may take either or both of 250A and
250B, in any order.
Lecture notes will be linked to the table below, as PDF
files. The notes for one day may be in the PDF file for an
earlier day.
October 2 
Overview of the course, intro
to Bayesian networks 

October 4 
Laws of probability theory, Bayesian
network for the earthquake scenario 
First homework assignment
distributed 
October 8 section notes 

October 9 
Simpson's paradox, explaining away,
formal definition of a Bayesian network 
Quiz 1 
October 11 
Conditional probability tables,
logistic regression, dseparation 
Second homework assignment
distributed 
October 15 section notes 

October 16 
Independence as absence of
information flow, examples of dseparation 
Quiz 2 
October 18 
Algorithm for computing p(XE)
in polytree networks 
Third homework
assignment distributed 
October 22 section notes coming soon 

October 23 
Merging nodes and cutset conditioning
for inference in loopy networks 
Quiz 3 
October 24 section notes on
inference via stochastic sampling 

October 25 
Principle
of maximum likelihood (ML). ML learning of
parameters for a Bayesian network. 
Fourth assignment 
October 26 section notes 

October 30 
Markov models of sentences, linear
regression viewed as a Bayesian network. 
Quiz 4 
November 1 
Expectationmaximization (EM) 
Fifth assignment 
November 6 
EM for Bayesian networks.
Contextbased language model. 
Quiz 5 
November 8 
21st century data analysis and the
election. EM for contextbased language models and
for mixture models. 
Sixth assignment 
Section notes on EM
and mixture models for Friday November 9 and
Monday November 12 

November 13 
Hidden Markov
models (HMMs). 
Quiz 6 
November 15 
Forward algorithm and Viterbi
algorithm. 
Seventh assignment,
due on November 27 
Section notes on HMM algorithms for
Nov. 16 

November 20 
EM training of HMMs. Linear dynamical
systems. 
Quiz 7 
November 22 
Thanksgiving: No class.  
November 27 
Reinforcement learning
(RL). 
Last assignment, due on
December 6 
November 29 
Policy iteration, restricted linear
value functions. 
Quiz 8 
Section notes on MDPs and RL for Nov.
30 

December 4 
Approximate policy evaluation. 

December 6 
Leastsquares policy iteration. 
Quiz 9 
December 12 
Wednesday from 11:30am to 2:30pm: Final exam. 