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Announcements 

QUESTION: "The k in k-decision list means each test in the list can only have up to k literals ANDed together, right?
For example, the 2-decision list in part b can have a test such as "If Hungry == T and Patrons == Some" but not "If Hungry == T and Patrons == Some and Price == $$" because the latter has 3 literals; is this correct?

ANSWER:      "k-decision lists denote the set of all boolean functions defined by decision lists, where each function in the list is a conjunction of size at most k."  So the answer is  Yes. http://people.csail.mit.edu/rivest/Rivest-DecisionLists.pdf

Homework 3 is posted.  DUE DATE  May 2ND.

Homework 2 is posted.  NOTE NEW DUE DATE  MARCH 23rd.

Homework 1 is posted.  NOTE, the homework has been revised, 1/24/11.

Assignments must be submitted before class on the day they are due.

Late assignments may be given zero credit.
 

Matlab Tutorials(1,2)Matlab HyperText Reference

 

Arrange access to matlab, java or C to solve the assignments.

 

Please do NOT submit any data provided to you.Thanks.

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Course Information 

Meetings:	MW, 4-5:15pm,  Mechanical Engineering 108
Professor:	Paul Schrater
E-mail:	schrater AT umn.edu
Office:	Primary: 211 Elliott Hall,  Secondary: 5-187 EE/CS Building
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, 3-4 PM in  2-209

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.

• Uncertainty (Chapter 13)
• Probabilistic Reasoning, Bayesian Networks (Chapter 14)
• Probabilistic Reasoning over Time (Chapter 15)
• Making Simple Decisions (Chapter 16)
• Making Complex Decisions, Markov Decision Processes (Chapter 17)
• Learning from Observations (Chapter 18)
• Statistical Learning Methods (Chapter 20)
• Reinforcement Learning (Chapter 21)
• If time, Language, Vision and Robotics (Chaps 22, 23, 24)

• Homeworks: There will be 4 homework assignments.  The post and due dates of the homeworks are given below.

  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

  
  You can submit the homeworks in soft-copy using Submit or in hard-copy at the beginning of the class the day the homework is due.
  Some of the homeworks will include programming assignments. All code must be written in Matlab, C or Java. Code written in any other language will not be graded and will receive zero credit. All code must be submitted using Submit.
  
  
• Midterm: There will be one in-class midterm on Mon, Mar 28. The exam will be closed book. However, you will be allowed to bring one sheet (letter size, the usual printer paper) of notes.
  
  
• Final Project: There will be a Final Project due Mon, May 10, 4pm.
  
  

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 10-15 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).





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• Four homeworks: 50% (12.5 x 4)
• Midterm exam: 20%
• Final Project: 30%

• Late Submission Policy:
  Late submissions of homeworks will be penalized using the following rule:
  • Late by 0-24 hrs: 25% deducted from actual score.
  • Late by 24-48 hrs: 50% deducted from actual score.
  • Late by more than 48 hrs: Will receive a zero.
  All late submissions must be submitted using Submit.
  
  
• Makeup Exam Policy: There will be no provision for make-up exams, except in extraordinary and documented circumstances.
  
  
• Academic Integrity Policy: Every student is expected to turn in his or her own work for all homeworks, assignments and exams. While students can generally discuss how to solve homework/assignment problems, they have to submit their own solutions/code for the problems. The University Student Conduct Code defines scholastic dishonesty as: submission of false records of academic achievement; cheating on assignments or examinations; plagiarizing; altering, forging, or misusing a University academic record; taking, acquiring, or using test materials without faculty permission; acting alone or in cooperation with another to falsify records or to obtain dishonestly grades, honors, awards, or professional endorsement. In this course, a student responsible for scholastic dishonesty will be assigned a penalty of an "F" or "N" for the course. If you have any questions regarding the expectations for a specific assignment or exam, please ask us. See the University of Minnesota Conduct Code for details.
  
  
• Special Circumstances: Students with special needs or circumstances should contact me as soon as possible to make any necessary arrangements. As outlined above, extensions are only granted for unforeseen and extraordinary circumstances. Other accommodations may be arranged in cooperation with disability services.
  
  

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Web Links

• On-line Matlab Help

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 Inference	Russell Norvig Ch. 14.1-14.3, Barber 3.1-3.3 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. 15.1-15.2, Barber 23.1-23.2
09.	Feb 16	Probabilistic Reasoning over Time (Contd.)	Russell Norvig 15.2-15.3
10.	Feb 21	Probabilistic Reasoning over Time: Part II	Russell Norvig Ch 15.4-15.5
11.	Feb 23	Making Simple Decisions	Russell Norvig Ch. 16
12.	Feb 28	Markov Decision Processes	Russell Norvig Ch. 17.1-17.2, Barber 7.5, 7.6
13.	Mar 02	NO CLASS
14.	Mar 07	Markov Decision Processes (Contd.)	Russell Norvig Ch. 17.3-17.4
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. 18.1-18.3
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. 20.1-20.2
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