Primary Texts:
|
|
|
|
| Week 1 | Introduction: What is AI? |
1
|
| Week 2 | Symbolic Programming and Lisp |
|
| Week 3 | Symbolic Programming and Lisp |
|
| Week 4 | Blind Search: Depth-First, Breadth-First |
3.1-3.5
|
| Week 5 | Informed Search: Hill-Climbing, Best-First, Genetic Algorithms |
4.1-4.3,
|
| Week 6 | Admissible Search: Branch-and-Bound, A*; Game Playing |
4.1, 6.1-6.3
|
| Week 7 | Logic: Propsitional, Resolution |
7.3-7.5
|
| Week 8 | Logic: Predicate, Unification, First-Order Resolution, Midterm Exam |
9
|
| Week 9 | Learning: Decision Trees, Ensemble Methods |
18.1-18.4
|
| Week 10 | Learning: Neural Networks, Perceptron, Back-propagation |
20.5
|
| Week 11 | Perception: Binary and Grayscale Vision |
24
|
| Week 12 | Perception: Smoothing, Edge Detection |
24
|
| Week 13 | Perception: 3D Information, Object Recognition |
24
|
| Week 14 | Uncertainty: Bayesian Inference Networks |
13, 14.1-14.3
|
| Week 15 | Uncertainty: Utility Theory; Philosophical Issues in AI |
16.2, 26
|