Fall 2015 Syllabus
This syllabus is subject to change. In particular, the midterm dates will not be finalized until a week or so into the course.
| Day | Topic | Reading | Slides | Discussion | Assignment | Due |
| Th 8/27 | Introduction to AI: Past, Present, Future |
Ch. 1, 26.3, 27.4 |
pdf pptx | F 9/4, 5pm | ||
| Tu 9/1 | Agents and Environments | Ch. 2 | pdf pptx | Dis0 (no handout) | HW1 | M 9/7, 11:59pm |
| Th 9/3 | Uninformed Search | Ch. 3.1-4 | pdf pptx | SBS-1 | ||
| Tu 9/8 | Informed Search and Heuristics | Ch. 3.5-6 | pdf pptx | HW2 | M 9/14, 11:59pm | |
| Th 9/10 | Local Search and Agents | Ch. 4 | pdf pptx | P1: Search and Games | F 9/25, 5pm | |
| Tu 9/15 | Game Playing | Ch. 5.1-5 | pdf pptx | Dis2 (sol) Prob1 (sol) Exam-Prac |
HW3 | T 9/22, 11:59pm |
| Th 9/17 | Constraint Satisfaction Problems | Ch. 6.1, 6.3-5 | pdf pptx | SBS-3 Exam-Prac |
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| Tu 9/22 | Propositional Logic: Semantics and Inference | Ch. 7.1-5, 7.6.1 | pdf pptx | HW4 | M 9/28, 11:59pm | |
| Th 9/24 | Logical Agents | Ch. 7.7 | pdf pptx | P2: A Logical Planning Agent | F 10/16, 5pm | |
| Tu 9/29 | First Order Logic | Ch. 8.1-3, 9.1 | pdf pptx | Dis4 (sol) | No HW | |
| Th 10/1 | MIDTERM | Practice Midterm (solutions) | T 9/29, 12:30pm | |||
| Tu 10/6 | Probability | Ch. 13.1-5 | pdf pptx | HW5 | W 10/14, 11:59pm | |
| Th 10/8 | Bayes Nets: Syntax and Semantics | Ch. 14.1-3 | pdf pptx | |||
| Tu 10/13 | Bayes Nets: Exact Inference | Ch. 14.3 | pdf pptx | HW6 | W 10/21, 11:59pm | |
| Th 10/15 | Bayes Nets: Approximate Inference | Ch. 14.4 | pdf pptx | P3: An HMM-based Agent | F 10/30, 5pm | |
| Tu 10/20 | Markov Models | Ch. 15.1-3, 22.1 | pdf pptx | Dis7 (sol) | HW7 | W 10/28, 11:59pm |
| Th 10/22 | Dynamic Bayes Nets and Particle Filtering | Ch. 15.5 | pdf pptx | |||
| Tu 10/27 | Decision Theory | Ch. 16.1-3, 16.5-6 | pdf pptx | Dis8 (sol) | HW8 | W 11/4, 11:59pm |
| Th 10/29 | Markov Decision Processes I | Ch. 17.1 | pdf pptx | P4: Decision-making and Learning Agent |
F 11/13, 5pm | |
| Tu 11/3 | Markov Decision Processes II | Ch. 17.2-3 | pdf pptx | Dis9 (sol) | HW9 | W 11/11, 11:59pm |
| Th 11/5 | Reinforcement Learning I | Ch. 21.1-3 | pdf pptx | |||
| Tu 11/10 | ML: Classification and Regression | Ch. 18.1-4, 18.6 | pdf pptx | No Discussion | F 11/20, 11:59pm | |
| Th 11/12 | ML: Classification and Neural Networks | Ch. 18.7 | pdf pptx | P5: Learning and Classification | F 12/4, 5pm | |
| Tu 11/17 | ML: Classification and Neural Networks (cont.) | Dis10 (sol) SBS-11 |
HW11 | W 12/02, 11:59pm | ||
| Th 11/19 | ML: Statistical Learning | Ch. 20 | pdf pptx | |||
| Tu 11/24 | Reinforcement Learning II | Ch. 21.4-5 | pdf pptx | No Discussion | No HW | |
| Th 11/26 | Thanksgiving Break | |||||
| Tu 12/1 | Advanced Applications: Natural Language Processing and Computer Vision | Optional: Ch. 23, 24 | pdf pptx | Dis11 (sol) | No HW | |
| Th 12/3 | Advanced Applications: Robotics & Conclusion | Optional: Ch. 25 | pdf pptx | |||
| F 12/18 |
FINAL EXAM (8-11am) |
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