Instructor: Dana H. Ballard
Office Hours: Wednesday and Friday 3-4pm or by appointment
Office: CSB 721
Extension: 5-3772
e-mail: dana@cs.rochester.edu
TA: Jessica Bayliss
Office hours:TBA
e-mail:bayliss@cs.rochester.edu
Credit
The grade for the course will be based on weekly assignments(80%) and a final(20%).
Course Outline
You should read the text assignment or handout before coming to class.
| Week | Date | Topic | Subtopic | Reading |
|---|---|---|---|---|
| 1 | 20 Jan | Artificial Intelligence | 1: What is intelligence? 2: Representation and Search | Ch 1 |
| 2 | 27 Jan | LISP exercises | 3: Basic LISP programming 4: Evaluation Issues | Ch 2 |
| 3 | 3 Feb | Search | 5: Introduction 6: Heuristic search | Ch 4(4.1-4.4) |
| 4 | 10 Feb | Games, Minimax, alpha/beta | 7: Minimax, Alpha-beta 8: Making symbols | Ch 4(4.5) |
| 5 | 17 Feb | Specialized Intelligence | 9: Expectation Maximization
10: Maximum Likelihood | Ch8 (8.1-8.2) |
| 6 | 24 Feb | Bayes Nets | 11: Intro to Bayes Nets
12: Bayes nets algorithms | Ch 8 (8.3) |
| 7 | 3 Mar | Reinforcement Learning | 13: Markov processes
14: Q-learning | Ch 5 (5.9) |
| 8 | 17 Mar | Pattern recognition | 15: Neural nets, Intro 16: Case study - Backgammon | Handout |
| 9 | 24 Mar | The Senses | 17: Basic Ontology 18: Introduction to Vision | Ch 9 (9.1-9.3) |
| 10 | 31 Mar | Vision | 19: Correlation 20: Algorithms(LAB) | Handout |
| 11 | 7 Apr | Natural Language | 21: Natural Language Parsing
22: Nat Language (LAB) | Ch 10 |
| 12 | 14 Apr | General Intelligence | 23: Logic 24: Representation and binding | Ch 3 |
| 13 | 21 Apr | Theorem Proving | 25: Resolution 26: Intro to planning | Ch 7 |
| 14 | 28 Apr | Planning | 27: Planning issues 28: Summary |