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