Instructor: Dana H. Ballard
Office Hours: Friday 3-4pm or by appointment
Office: CSB 721
Extension: 5-3772
e-mail: dana@cs.rochester.edu
The goal of Artificial Intelligence has always been to imitate human intelligence but the methodology has changed radically. Initially learning was a taboo subject but now learning forms the centerpiece of AI research. Initially human brains were regarded as idiosynchratic but now human brain function is seeing major emphasis. Initially models of behavior were exclusively symbolic, but now sub-symbolic models receive the major focus. The result of all these trends is that AI researchers need a background in mathematically oriented subjects such as information theory, probability theory, and optimization theory. The goal of this course is to introduce this background in order to describe computational models of intelligent behavior and how they relate to structures in the brain.
Course Credit: Credit will be based on ten assignments, a midterm (10 %) and a final (15%). The assignments will be due Sunday midnight of every week. Some assignments will require computer programming in Matlab or C.
| Week | Date | Topic | Reading | Assignment | Instructor |
|---|---|---|---|---|---|
| 0 | 15 Jan | Introduction | NatComp Ch1 | Yu | |
| 1 | 20 Jan | Probability Theory | Nat Comp Ch 2 Prob Thy | Yu | |
| 1 | 22 Jan | Prob Thy | Expectation Maximization Notes | Homework #1 | Yu |
| 2 | 27 Jan | Information Thy 1 | Notes | Homework #2 | Sprague |
| 2 | 29 Jan | Information Thy 2 | Notes | Sprague | |
| 3 | 3 Feb | Learning Thy1 | Haykin 2.14 Notes | Prob Set | Ogihara |
| 3 | 5 Feb | Learning Thy 2 | NatComp Ch1 | Ogihara | |
| 4 | 10 Feb | Linear Algebra | Notes | ICA Homework | Sprague |
| 4 | 12 Feb | Dynamical Systems | NotesNatComp Ch5 | Sprague | |
| 5 | 17 Feb | Optimization | NatComp Ch5 | Prob Set | Yu |
| 5 | 19 Feb | Optimization | NatComp Ch5 | Yu | |
| 6 | 24 Feb | CAM 1: Hopfield Nets | Notes61 | Homework | Gildea |
| 6 | 26 Feb | Perceptrons | Notes62 Conjugate Gradients | Gildea | |
| 7 | 2 Mar | Review | |||
| 7 | 4 Mar | MidTerm | |||
| 8 | 16 Mar | BackProp | Flying machine | Ballard | |
| 8 | 18 Mar | Backprop | Ballard | ||
| 9 | 23 Mar | SOM | Notes8.1 | Trav. Salesman | Ballard |
| 9 | 25 Mar | Support vectors | Notes8.1 | Ballard | |
| 10 | 30 Mar | Markov models | Notes9.1 | Homework | Ballard |
| 10 | 1 Apr | Hidden Markov models | Notes9.2 | Ballard | |
| 11 | 6 Apr | Reinforcement Learning | Homework: RL | Ballard | |
| 11 | 8 Apr | RL2 | Ballard | ||
| 13 | 13 Apr | Genetic Algorithms | Notes11.1 | Homework: GA | Ballard |
| 13 | 15 Apr | Genetic Algorithms | Adami ppr | Ballard | |
| 12 | 20 Apr | Games | Homework: Games | Ballard | |
| 12 | 22 Apr | Games | Ballard | ||
| 14 | 27 May | Review | Front , Back , Summary | Ballard |