ECE 469: Artificial Intelligence

• Instructor Info
• Textbook
• Grading
• Projects
• Schedule

Instructor Info

Instructor: Carl Sable
e-mail: CarlSable.Cooper@gmail.com
Office: Room 614

Textbook

Grading

Projects

• Project #1
  Click here for a suggested schedule and other helpful information
  Click here for a readme file briefly describing the files provided below
  Click here to download my Checkers executable (compiled under Cygwin)
  Click here to download my Othello executable (compiled under Cygwin)
  Click here for cygwin1.dll (if you need it; see the readme)
  Click here to download my Checkers executable (compiled under Ubuntu)
  Click here to download my Othello executable (compiled under Ubuntu)
  Sample Checkers board #1
  Sample Checkers board #2
  Sample Checkers board #3
  Sample Checkers board #4
  Sample Checkers board #5
  Sample Checkers board #6
  Sample Checkers board #7
  Sample Checkers board #8
  Sample Checkers board #9
  Sample Checkers board #10
  Sample Othello board #1
  Sample Othello board #2
• Project #2
  Click here for my annoated version of Figure 18.24
  Click here for the raw data of the Wisconsin Diagnostic Breast Cancer (WDBC) dataset
  Click here for a description of the data set by its creators
  Click here for my pre-processed WDBC training set file
  Click here for my pre-processed WDBC test set file
  Click here for my initial neural network for the WDBC dataset
  Click here for my trained neural network for the WDBC dataset
  (with a learning rate of 0.1 after 100 epochs)
  Click here for my results file (using the above trained neural network)
  Click here for a mini WDBC training set with one document (to help with debugging)
  Click here for the neural network trained on the mini WDBC training set
  (with a learning rate of 0.1 after 1 epoch)
  Click here for my grades training file
  Click here for my grades test file
  Click here for my inital neural network for the grades dataset
  Click here for my trained neural network for the grades dataset
  (with a learning rate of 0.05 after 100 epochs)
  Click here for my results file (using the above trained neural network)

Schedule

• Wednesday, September 2
  Topic: Introduction
  Suggested Reading: R&N Ch 1 and Computing Machinery and Intelligence (Turing, 1950)
• Thursday, September 3
  Topic: Intelligent Agents
  Suggested Reading: R&N Ch 2
• Wednesday, September 9
  Intelligent Agents (continued)
  Topic: Simple Search Strategies
  Suggested Reading: R&N Sections 3.1 - 3.4 and 4.4.1
• Thursday, September 10
  Simple Search Strategies (continued)
• Wednesday, September 16
  Simple Search Strategies (continued)
  Topic: Advanced Search Strategies
  Suggested Reading: R&N Sections 3.5 - 3.6, 4.1 - 4.2, and 4.5
• Thursday, September 17
  Advanced Search Strategies (continued)
• Wednesday, September 23
  Advanced Search Strategies (continued)
  Topic: Games
  Suggested Reading: R&N Chapter 5
• Thursday, September 24
  Games (continued)
• Wednesday, September 30
  Games (continued)
• Thursday, October 1
  Project #1 assigned and discussed
• Wednesday, October 7
  Topic: Probability
  Suggested Reading: R&N Chapter 13
• Thursday, October 8
  Probability (continued)
• Thursday, October 15
  Topic: Bayesian Networks
  Suggested Reading: R&N Chapter 14
• Wednesday, October 21
  Bayesian Networks (continued)
  Topic: Bayesian Learning
  Suggested Reading: R&N Chapter 20
• Thursday, October 22
  Bayesian Learning (continued)
• Wednesday, October 28
  Bayesian Learning (continued)
  Topic: Decision Trees
  Suggested Reading: R&N Sections 18.1 - 18.3 and 18.10
• Thursday, October 29
  Decision Trees (continued)
• Wednesday, November 4
  Decision Trees (continued)
  Topic: Neural Networks
  Suggested Reading: R&N Section 18.7
• Thursday, November 5
  Neural Networks (continued)
• Wednesday, November 11
  Neural Networks (continued)
  Topic: Miscellaneous Machine Learning
  Suggested Reading: R&N Sections 15.3, 18.8, 18.9, and 20.3.3
• Thursday, November 12
  Project #2 assigned and discussed
• Wednesday, November 18
  Miscellaneous Machine Learning (continued)
• Thursday, November 19
  Topic: Natural Languages
  Suggested Reading: R&N Chapter 23
• Wednesday, November 25
  Natural Languages (continued)
• Wednedsay, December 2
  Topic: Statistical Natural Language Processing
  Suggested Reading: R&N Chapter 22
• Wednesday, December 2
  Statistical Natural Language Processing (continued)
• Thursday, December 3
  Topic: Philosophy and AI
  Suggested Reading: R&N Chapter 26 and Minds, Brains, and Programs (Searle, 1980)
• Wednesday, December 9
  Philosophy and AI (continued)
• Thursday, December 10
  Philosophy and AI (continued)
• Monday, December 14
  FINAL EXAM