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General Instructions for Submitting ALL Assignments

CSUG Users with new accounts need to reset their passwords at:

        http://www2.csuglab.cornell.edu/userinfo.htm

We will be using the Course Management System for all submissions, grade distribution, regrades, etc. So please check out the CMS site ASAP.  Don’t wait until it's time to submit your homework. If you encounter any problems, follow the instructions link below. If problem persists contact the CS472 staff at cs472@cs.cornell.edu.

Please archive (.zip or .gzip) all files that you submit.  Please note that there is a maximum size set for each of the archives.

Regrades

If you believe that an error was made in grading your assignment, you should definitely submit the assignment for a re-grade. This is to be done electronically, following the instructions that will appear here.  Before asking for a regrade, please check the on-line solution. Regrades should be submitted within one week from when the assignment was handed back.  Please submit regrade requests with care --- your new grade could easily be lower after the regrade since we reserve the right to look at the entire assignment, not just the parts in question.

Homework 1. 

A pdf version of homework 1 can be found here, and a postcript version here.  The paper for the critique can be found here.

Guidelines for writing a good critique
A couple of examples of good critiques of technical papers (courtesy of  Ves Stoyanov and Oren Kurland)

·         Example 1

·         Example 2

·         Example 3

Homework 2

This homework is on CMS.  A pdf version of the paper for the critique can be found here.

Homework 3

This homework is on CMS.  A pdf version of the paper for the critique can be found here.

Homework 5

This homework is on CMS.  A pdf version of the paper for the critique can be found here.

Lecture Notes

Date

Topic 

Reading/Assignments

Fri, Aug 27

Introduction [.pdf]

R&N, chapter 1

Mon, Aug 30;
Wed, Sept 1
Fri, Sept 3

Problem-solving as search: uninformed search

[.pdf]

R&N, chapter 2.

R&N, chapter 3.

Mon, Sept 6;
Wed, Sept 8

Problem-solving as search: informed search
     best-first search algorithms
[.pdf]

R&N, chapter 4.1-4.2.
 

Fri, Sept 10
Mon, Sept 13
Wed, Sept 15

Problem-solving as search: informed search
     local search algorithms

     genetic algorithms
[.pdf]

R&N, chapter 4.3.
And p. 120.

Fri, Sept 17
Mon, Sept 20

Problem-solving as search: constraint satisfaction
[.pdf]

R&N, chapter 5.

Wed, Sept 22
Fri, Sept 24
Mon, Sept 27

Adversarial search [.pdf]

R&N, chapter 6.

Wed, Sept 29
Fri, Oct 1

Machine learning:
   introduction
   k-nn
   decision trees
[.pdf]

R&N, 18.1-18.3
R&N, Chapter 20, pgs. 733-735

Mon, Oct 4

***In-class prelim***
Covers material through 9/27.

Weds, Oct 6

Machine learning:
   Guest lecture: Art Munson
   more decision trees [see slides from 10/1]
   finished on 10/13

Fri, Oct 8
Mon, Oct 11 		FALL Break  

Weds, Oct 13
Fri, Oct 15 (cancelled: Claire sick)

Machine learning:
   version spaces
[.pdf]

R&N, 19.1

Mon, Oct 18

Machine learning:
   Guest lecture: Ves Stoyanov
   SVM's, bagging, boosting
[.pdf]

R&N, 18.4
 R&N, 20.6-20.7

Weds, Oct 20

Machine learning:
   version spaces
   [see slides from 10/13]
   example from class [.txt]

Fri, Oct 22
Mon, Oct 25
Wed, Oct 27

Machine learning:
   artificial neural networks [.pdf]
   perceptron example from class [.txt]

R&N, 20.5

Fri, Oct 29
Mon, Nov 1

Machine learning:
   reinforcement learning [.pdf]

R&N, 21.1-21.2; 21.4 (Some of you may also need to read parts of Ch. 17 as referenced by Ch. 21.)

Weds, Nov 3

Fri, Nov 5
Mon, Nov 8
Weds, Nov 10
Fri, Nov 12

Knowledge-based systems [.pdf]

R&N, Ch 7-9

Mon, Nov 15
Weds, Nov 17

Fri, Nov 19

Planning [.pdf]

R&N, Ch 11
Mon, Nov 22 ***In-class prelim***
Wed, Nov 24
Fri, Nov 26		 Thanksgiving Break  
Mon, Nov 29 finished Planning  
Weds, Dec 1
Fri, Dec 3		 Natural language processing [.pdf] R&N, Ch 22.0, 22.1,22.6,22.7, 23.2-23.4 (up to, but not including statistical MT)

CS473 Project Information

Tentative Project Due Dates

All project  materials should be submitted via CMS using the General Instructions for submitting assignments in the homework section above.

Some project ideas

Interesting web links

  • AI Alert - The American Association for Artificial Intelligence's semimonthly list of interesting AI news articles from around the internet.
  • Decision tree that guesses which evil dictator or TV sitcom character you're thinking of. (Link submitted by Val Ebert and John Woschinko.)
  • The Temple of Alife - several nice Java applets that demonstrate some of the more "colorful" sides of artificial life. (Link submitted by Jason Rohrer)
  • "Self-taught" checkers - a New York Times article about Fogel's evolutionary neural network system that learned to play checkers by playing against itself (note that you will need to sign up for the NY Times library service to access this article, but it's free). (Link submitted by Jason Rohrer)
  • Backgammon info (Link submitted by Dave Sungarian)
  • Is your new AIM pal an artificial intelligence program - What do you get when you mix AOL, AppleScript, and artificial intelligence (AI)? Psychoanalysis, Internet style. (Link submitted by Stewart Munoz)
  • Tools for learning Computational Intelligence - Here are some applets that are designed as tools for learning and exploring some concepts in artificial intelligence. They are part of the online resources for Computational Intelligence. Feedback is welcome. (Link submitted by Kevin O'Neill)

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