Draft Course Schedule (Fall 2014)
The following schedule is subject to revision!
The readings, unless explicitly specified, come from Artificial Intelligence: A Modern Approach, 3rd ed. by Russell and Norvig.
The lecture videos can be found under the "Video" column here; and additionally, under the Lecture Videos tab along with lecture videos from past semesters.
Under the videos column, there are additional Step-By-Step videos made by Pieter Abbeel which supplement the lecture's materials. See the list of Step-By-Step videos here.
| Day | Topic | Reading | Slides | Videos | Assignment | Due |
| Thu 8/28 | Introduction to AI: Past, Present, Future | Ch. 1, 26.3, 27.4 | Lecture 1 | P0: Tutorial | 9/3 5pm | |
| Tu 9/2 | Agents and environments | Ch. 2 | HW1: Agents and Search | 9/10 | ||
| Th 9/4 | Uninformed Search | Ch. 3.1-4 | SBS-1 |
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| Tu 9/9 | A* Search and Heuristics | Ch. 3.5-6 | SBS-2 |
HW2: Heuristic and local search | 9/15 | |
| Th 9/11 | Local search; search-based agents | Ch. 4 | P1: Search and games | 9/24 5pm | ||
| Tu 9/16 | Game playing | Ch. 5.1-5 | SBS-3 | HW3: Games and CSPs | 9/22 | |
| Th 9/18 | Constraint satisfaction problems | Ch. 6.1, 6.3-5 | ||||
| Tu 9/23 | Propositional logic: semantics and inference | Ch. 7.1-5 (7.5.2 optional), 7.6.1 | HW4: Propositional logic | 9/29 | ||
| Th 9/25 | Propositional planning and logical agents | Ch. 7.7 | P2: A logical planning agent | 10/8 5pm | ||
| Tu 9/30 | First-order logic | Ch. 8.1-3,9.1 | ||||
| Th 10/2 | Midterm | |||||
| Tu 10/7 | Probability | Ch. 13.1-5 | HW5: Probability and Bayes nets | 10/13 | ||
| Th 10/9 | Bayes nets: Syntax and semantics | Ch. 14.1-3 | SBS-4 | |||
| Tu 10/14 | Bayes nets: Exact inference | Ch. 14.3 | SBS-5 SBS-6 |
HW6: Bayes net inference | 10/20 | |
| Th 10/16 | Bayes nets: Approximate inference | Ch. 14.4 |
SBS-7 SBS-8 |
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| Tu 10/21 | Markov Models | Ch. 15.1-3, 15.5, 22.1 | HW7: HMMs etc. | 10/27 | ||
| Th 10/23 | Speech recognition | Ch. 23.5 | P3: An HMM-based agent | 11/5 5pm | ||
| Tu 10/28 | Decision theory | Ch. 16.1-3, 16.5-6 | HW8: Decision theory and MDPs | 11/3 | ||
| Th 10/30 | Markov decision processes I | Ch. 17.1 | ||||
| Tu 11/4 | Markov decision processes II | Ch. 17.2-3 | HW9: MDPs and reinforcement learning | 11/10 | ||
| Th 11/6 | Reinforcement learning I | Ch. 21.1-3 | P4: Decision-making and learning agent | 11/19 5pm | ||
| Tu 11/11 | Veterans Day | |||||
| Th 11/13 | Machine learning: Classification and regression | Ch. 18.1-4, 18.6 | ||||
| Tu 11/18 | Machine learning: Neural networks | Ch. 18.7 | SBS-9 | HW10: Machine learning | 11/24 | |
| Th 11/20 | Machine learning: Statistical learning | Ch. 20 | SBS-10 SBS-11 |
P5: Learning agent, contd. | 12/5 5pm | |
| Tu 11/25 | Reinforcement learning II | Ch. 21.4-5 | ||||
| Th 11/27 | Thanksgiving Day | |||||
| Tu 12/2 | Advanced applications: Natural language understanding | Optional: Ch. 22, 23 | ||||
| Th 12/4 | Advanced applications: Vision and Robotics | Optional: Ch. 24, 25 | ||||
| Fri 12/19, 8:00 a.m., location TBA | Final Exam (solutions) | |||||