Primary Texts:
|
|
|
|
| Week 1 | Introduction: What is AI?, Strong vs. Weak |
1
|
| Week 2 | Introduction: Areas, Brief History, Paradigms |
1
|
| Week 3 | Symbolic Programming and Lisp |
Handout
|
| Week 4 | Blind Search: Depth-First, Breadth-First |
3
|
| Week 5 | Informed Search: Hill-Climbing, Beam, Best-First, Genetic Algorithms |
4, 20.8
|
| Week 6 | Admissible Search: Branch-and-Bound, A*; Game Playing |
4, 5
|
| Week 7 | Logic: Propsitional, Resolution, Predicate |
6
|
| Week 8 | Logic: Unification, First-Order Resolution, Midterm Exam |
7, 9
|
| Week 9 | Learning: Nearest Neighbor, Naive Bayes |
18
|
| Week 10 | Learning: Tree and Rule Induction, Information Retrieval |
18
|
| Week 11 | Learning: Neural Networks, Perceptron, Back-propagation |
19
|
| Week 12 | Image Understanding: Binary and Grayscale Vision |
24
|
| Week 13 | Image Understanding: Stereo Vision, Optical Flow |
24
|
| Week 14 | Uncertainty: Bayesian Inference Networks |
14, 15
|
| Week 15 | Uncertainty: Utility Theory; Philosophical Issues in AI |
16, 26
|
| Dec 13, 4-6 PM | Final Exam |
|
|
|