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 4, by 11:59 PM
  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 for cygstdc++-6.dll (if you need it; see the readme)
  Click here for cyggcc_s-seh-1.dll (if you need it; see the readme)
  Click here to download my Checkers executable (compiled under Ubuntu 20.04)
  Click here to download my Othello executable (compiled under Ubuntu 20.04)
  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
  Due the night of Friday, December 8, before midnight
  Click here for my annotated version of Figure 18.24 from the 3rd edition of the textbook
  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 initial 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

Part 1: Introductory Concepts

• Wednesday, August 30
  Topic: Course Introduction (slides)
  Suggested Reading: R&N Chapter 1
  See the syllabus as a single document
• Thursday, August 31
  Topic: Intelligent Agents (slides)
  Suggested Reading: R&N Chapter 2

• Wednesday, September 6
  Topic: Simple Search Strategies (slides)
  Suggested Reading: R&N Sections 3.1 - 3.4 and 4.4.1
• Thursday, September 7
  Simple Search Strategies (continued)
• Wednesday, September 13
  Simple Search Strategies (continued)
  Topic: Advanced Search Strategies (slides)
  Suggested Reading: R&N Sections 3.5 - 3.6, 4.1 - 4.2, and 4.5
• Thursday, September 14
  Advanced Search Strategies (continued)
• Wednesday, September 20
  Advanced Search Strategies (continued)
  Topic: Games (slides)
  Suggested Reading: R&N Chapter 5
• Thursday, September 21
  Games (continued)
• Wednesday, September 27
  Games (continued)
• Thursday, September 28
  Games (continued)
  Quiz #1
• Wedesday, October 4
  Games (continued)
  Project #1 assigned and discussed

• Thursday, October 5
  Topic: Probability (slides)
  Suggested Reading: R&N Chapter 12
• Wednesday, October 11
  Topic: Probability (continued)
  Topic: Bayesian Networks (slides)
  Suggested Reading: R&N Chapter 13
• Thursday, October 12
  Topic: Bayesian Networks (continued)
• Wednesday, October 18
  Topic: Hidden Markov Models (slides)
  Suggested Reading: R&N Sections 14.1 - 14.3 and "An Introduction to Hidden Markov Models and Bayesian Networks" (Ghahramani 2001)

• Thursday, October 19
  Topic: Machine Learning Concepts (slides)
  Suggested Reading: R&N Sections 19.1 - 19.2 and 19.4
• Wednesday, October 25
  Topic: Decision Trees (slides)
  Suggested Reading: R&N Section 19.3
• Thursday, October 26
  Topic: Bayesian Learning (slides)
  Suggested Reading: R&N Chapter 20
• Wednesday, November 1
  Bayesian Learning (continued)
  Topic: Feedforward Neural Networks (slides)
  Suggested Reading: R&N Sections 21.1 - 21.2
  Click here for my annotated version of Figure 18.24 from the 3rd edition of the textbook
• Thursday, November 2
  Feedforward Neural Networks (continued)
• Wedesday, November 8
  Feedforward Neural Networks (continued)
  Topic: Deep Learning (slides)
  Suggested Reading: R&N Sections 21.3 and 21.5
• Thursday, November 9
  Deep Learning (continued)
  Quiz #2
• Wedesday, November 15 (first half of lecture)
  Project #2 assigned and discussed

• Wednesday, November 11 (second half of lecture)
  Topic: Conventional Statistical Natural Language Processing (slides)
  Suggested Reading: R&N Section 23.1
• Thursday, November 16
• Conventional Statistical Natural Language Processing (continued)
• Tuesday, November 21 (Thursday schedule)
• Conventional Statistical Natural Language Processing (continued)
  Topic: Conventional Computational Linguistics (slides)
  Suggested Reading: R&N Sections 23.2 - 23.6
• Wednesday, November 29
  Conventional Computational Linguistics (continued)
  Topic: Deep Learning and NLP (slides)
  Suggested Reading: R&N Chapter 24
• Thursday, November 30
  Deep Learning and NLP (continued)
• Wednesday, December 6 (first half)
  Deep Learning and NLP (continued)

• Wednesday, December 6 (second half)
  Topic: Philosophy and Ethics of AI (slides)
  Suggested Reading: R&N Chapter 27 and "Computing Machinery and Intelligence" (Turing, 1950)
• Wednesday, December 13
  Philosophy and Ethics AI (continued)
• Thursday, December 14
  Quiz #3