ECE 469: Artificial Intelligence

• Instructor Info
• Textbook
• Grading
• Projects
• Schedule

Instructor Info

Instructor: Carl Sable
e-mail: carl.sable@cooper.edu
Office: Room 614

Textbook

Grading

Projects

• Project #1
  Due the night of Saturday, November 2, before midnight
  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

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