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 16.04)
  Click here to download my Othello executable (compiled under Ubuntu 16.04)
  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 5
  Topic: Introduction
  Suggested Reading: R&N Ch 1 and Computing Machinery and Intelligence (Turing, 1950)
• Monday, September 10
  Topic: Intelligent Agents
  Suggested Reading: R&N Ch 2
• Wednesday, September 12
  Intelligent Agents (continued)
  Topic: Simple Search Strategies
  Suggested Reading: R&N Sections 3.1 - 3.4 and 4.4.1
• Monday, September 17
  Simple Search Strategies (continued)
• Wednesday, September 19
  Topic: Advanced Search Strategies
  Suggested Reading: R&N Sections 3.5 - 3.6, 4.1 - 4.2, and 4.5
• Monday, September 24
  Advanced Search Strategies (continued)
• Wednesday, September 26
  Topic: Games
  Suggested Reading: R&N Chapter 5
  Click here for textbook's alpha beta example with correct values.
  Click here for my alpha beta example.
• Monday, October 1
  Games (continued)
• Wednesday, October 3
  Games (continued)
• Monday, October 8
  Project #1 assigned and discussed
• Wednesday, October 10
  Topic: Probability
  Suggested Reading: R&N Chapter 13
• Monday, October 15
  Topic: Bayesian Networks
  Suggested Reading: R&N Chapter 14
• Wednesady, October 17
  Bayesian Networks (continued)
• Monday, October 22
  Topic: Bayesian Learning
  Suggested Reading: R&N Chapter 20
• Wednesday, October 24
  Bayesian Learning (continued)
  Topic: Decision Trees
  Suggested Reading: R&N Sections 18.1 - 18.3 and 18.10
• Monday, October 29
  Decision Trees (continued)
• Wednesday, October 31
  Decision Trees (continued)
  Topic: Neural Networks
  Suggested Reading: R&N Section 18.7
• Monday, November 5
  Neural Networks (continued)
• Wednesday, November 7
  Neural Networks (continued)
• Monday, November 12
  Project #2 assigned and discussed
• Wednesday, November 14
  Topic: Miscellaneous Machine Learning Suggested Reading: R&N Sections 15.3, 18.8, 18.9, and 20.3.3
• Monday, November 19
  Miscellaneous Machine Learning (continued)
• Monday, November 26
  Topic: Natural Languages
  Suggested Reading: R&N Chapter 23
• Wednedsay, November 28
  Natural Languages (continued)
• Monday, December 3
  Topic: Statistical Natural Language Processing
  Suggested Reading: R&N Chapter 22
• Wednesday, December 5
  Statistical Natural Language Processing (continued)
• Monday, December 10
  Statistical Natural Language Processing (continued)
• Wednesday, December 12
  Topic: Philosophy and AI
  Suggested Reading: R&N Chapter 26
• Monday, December 17
  Philosophy and AI (continued)
• Wednesday, December 19
  FINAL EXAM