Primary Texts:
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| Week 1 | Introduction: What is AI?, Strong vs. Weak |
1
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| Week 2 | Introduction: Areas, Brief History, Paradigms |
1
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| Week 3 | Symbolic Programming and Lisp |
2
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| Week 4 | Blind Search: Depth-First, Breadth-First |
4.1, 4.2
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| Week 5 | Informed Search: Hill-Climbing, Best-First, Genetic Algorithms |
4.3, 4.4,
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| Week 6 | Admissible Search: Branch-and-Bound, A*; Game Playing |
4.4, 4.5
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| Week 7 | Logic: Propsitional, Resolution, Predicate |
3.1-3.3
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| Week 8 | Logic: Unification, First-Order Resolution, Midterm Exam |
3.4-3.6
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| Week 9 | Learning: Nearest Neighbor, Naive Bayes |
5.1
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| Week 10 | Learning: Tree and Rule Induction, Information Retrieval |
5.4
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| Week 11 | Learning: Neural Networks, Perceptron, Back-propagation |
5.5
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| Week 12 | Image Understanding: Binary and Grayscale Vision |
9.1, 9.2, 9.5
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| Week 13 | Image Understanding: Stereo Vision, Optical Flow |
9.9, 9.10
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| Week 14 | Uncertainty: Bayesian Inference Networks |
8.3
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| Week 15 | Uncertainty: Utility Theory; Philosophical Issues in AI |
8.4
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| Dec 19, 9-11 AM | Final Exam |
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