
Mechanical Engineering 108 Secondary: 5187 EE/CS
Building
Meetings:
MW, 45:15pm,
Professor:
Paul
Schrater
Email:
schrater AT
umn.edu
Office:
Primary: 211 Elliott
Hall,
Office
hours:
M 05:15 P.M.  6:15 P.M., or by appointment, in
S211 Elliott Hall
TA:
Steve Damer
damer AT cs dot umn.edu
TA office hrs
Mondays, 34 PM
in 2209
Textbook:
The required textbook for this course
is Artificial Intelligence: A Modern Approach, Second Edition,
by
Stuart Russell and Peter Norvig.
You may benefit from the book Bayesian Reasoning and Machine
Learning, by David Barber. David has generalously kept
a pdf of the book online here.
Homework  Post Date  Due Date  Due Time  Total Time 

HW1  Wed, Jan 19  Wed, Jan 26  4 pm  7 days 
HW2  Mon, Feb 21  Mon, Mar 8  4 pm  14 days 
HW3  Mon, Mar 28  Mon, Apr 11  4 pm  14 days 
HW4  Mon, Apr 18  Mon, May 2  4 pm  14 days 
Final
Project Assignment:
Your
final project will involve one of the following
1) Simulation or experiments.
2) Literature survey (with critical evaluation) on a given topic.
3) Theoretical work (detailed derivations, extensions of existing work, etc)
In all cases, the work should be written up as a 1015 page paper. More difficult projects will get better grades if sucessfully completed. You will be evaluated in terms of the care with which you set up and thought through the goals and implementation, and in terms of the competence of the execution. Regardless of form the write up must include a survey of related literature results. This survey counts for 30% of your project grade and should show your ability to independently find, read, understand, and summarize papers in the primary literature related to your project topic.
The project schedule is:
Feb. 24: Topic selection. One or two pages explaining the project with
a list of references.
May 9: Final report (10 to 15 pages).
Schedule Spring 2011
Date Topic Reading 01. Jan 19 Course Overview, Probability Basics Russell Norvig Ch. 13 Homework 1 posted. Due Jan 26 at 4pm. 02. Jan 24 Probability Basics Russell Norvig Ch. 13 03. Jan 26 Probabilistic Reasoning
Exact InferenceRussell Norvig Ch. 14.114.3, Barber 3.13.3 Russell Norvig Ch. 14.4 04. Jan 31 Exact Inference
05. Feb 02 Sum Product Algorithm (Contd.) Factor Graph paper, Barber Ch. 5.1 06. Feb 07 Approximate Inference: Stochastic Russell Norvig 14.5, Markov Chain slides 07. Feb 09 Approximate Inference: MCMC MCMC Survey, Notes on Gibbs sampling, MCMC and Gibbs sampling 08. Feb 14 Probabilistic Reasoning over Time: Part I Russell Norvig Ch. 15.115.2, Barber 23.123.2 09. Feb 16 Probabilistic Reasoning over Time (Contd.) Russell Norvig 15.215.3 10. Feb 21 Probabilistic Reasoning over Time: Part II Russell Norvig Ch 15.415.5
11. Feb 23 Making Simple Decisions Russell Norvig Ch. 16 12. Feb 28 Markov Decision Processes Russell Norvig Ch. 17.117.2, Barber 7.5, 7.6 13. Mar 02 NO CLASS
14. Mar 07 Markov Decision Processes (Contd.) Russell Norvig Ch. 17.317.4 15. Mar 09 Game Theory Russell Norvig Ch. 17.6 Mar 14 Spring Break Mar 16 Spring Break 16. Mar 21 Game Theory (Contd.) 17. Mar 23 Midterm Review 18. Mar 28 Midterm
19. Mar 31 Learning from Observations Russell Norvig Ch. 18.118.3 20. Apr 04 Ensemble Methods, Boosting Russell Norving Ch. 18.4, Boosting Overview 21. Apr 06 Learning Theory Russell Norvig Ch. 18.5 22. Apr 11 Statistical Learning Russell Norvig Ch. 20.120.2 23. Apr 13 Neural Networks Russell Norvig Ch. 20.5 24. Apr 18 Neural Networks (Contd.)
25. Apr 20 Reinforcement Learning Russell Norvig Ch. 21 26. Apr 25 Reinforcement Learning (Contd.) 27. Apr 27
28. May 02 POMDPs
29. May 04 Learning with Hidden Variables Russell Norvig Ch. 20.3, Gentle Tutorial,EM Demystified May 09 Final Project Due