Spring 2017 Syllabus
This syllabus is subject to change! Note that unreleased project out and due dates are just guesses and will likely change somewhat.
| Day | Topic | Optional Reading | Slides | Video | Assignment | Due |
| W 1/18 | Introduction to AI | Ch. 1 | PPT & PDF | Math Self-Diagnostic P0: Tutorial |
(ungraded) F 1/20 5pm |
|
| M 1/23 | Agents and Search | Ch. 3.1-4 (2e: Ch. 3) | PPT & PDF | HW1 (section 1) | T 1/31 11:59pm | |
| W 1/25 | A* Search and Heuristics | Ch. 3.5-6 (2e: Ch. 4.1-2) | PPT & PDF | P1: Search Contest 1: Search |
F 2/3 5pm Su 2/5 11:59pm |
|
| M 1/30 | Constraint Satisfaction Problems | Ch. 6.1 (2e: Ch. 5.1) | PPT & PDF | HW2 (section 2) | M 2/6 11:59pm | |
| W 2/1 | CSPs II | Ch. 6.2-5 (2e: Ch. 5.2-4) | PPT & PDF | |||
| M 2/6 | Game Trees: Minimax | Ch. 5.2-5 (2e: Ch. 6.2-5) | HW3 (section 3/ exam-prep 1) | M 2/13 11:59pm | ||
| W 2/8 | Game Trees: Expectimax; Utilities | Ch. 5.2-5 (2e: Ch. 6.2-5) | PPT & PDF | F 2/17 5pm Su 2/19 11:59pm |
||
| M 2/13 | Markov Decision Processes | Ch. 16.1-3 | PPT & PDF | (section 4 / exam-prep 2) | ||
| W 2/15 | Markov Decision Processes II | Sutton and Barto Ch. 3-4 | PPT & PDF | T 2/21 11:59pm | ||
| M 2/20 | Academic Holiday (No lecture) | |||||
| W 2/22 | Reinforcement Learning | Ch. 17.1-3, S&B Ch. 6.1,2,5 | PPT & PDF | HW5 (section 5 / exam-prep 3) | T 3/7 11:59pm | |
| M 2/27 | Reinforcement Learning II | PPT & PDF |
F 3/3 |
|||
| W 3/1 | RL/Probability/Bayes Nets | Ch. 13.1-5 (2e: Ch. 13.1-6) | PPT & PDF | (section 6 / exam-prep 4) | ||
| M 3/6 | RL/Bayes' Nets: Representation | Ch. 14.1-2,4 |
M 3/13 11:59pm F 4/7 5:00pm |
|||
| W 3/8 | Bayes' Nets: Independence | Ch. 14.3, Jordan 2.1 | PPT & PDF | (section 7 / exam-prep 5) | ||
| M 3/13 | Bayes' Nets: Inference | Ch. 14.4-5 | PPT & PDF | |||
| W 3/15 | MIDTERM (7-8:30p) | |||||
| M 3/20 | Bayes' Nets: Inference/Sampling | Ch. 15.1-3, 6 | PPT & PDF | HW7 | M 4/10 11:59pm | |
| W 3/22 | Bayes' Nets: Sampling and Decision Networks | Ch. 15.2-5 | PPT & PDF | |||
| M 3/27 | Spring Break | |||||
| W 3/29 | Spring Break | |||||
| M 4/3 | Decision Networks and Value of Information | Ch. 15.2,6 | PPT & PDF | HW8 (section 8 / exam-prep 6) | T 4/18 11:59pm | |
| W 4/5 | Markov Models and HMMs | Ch. 15.2,6 | PPT & PDF |
Final Contest |
M 4/17 5pm Su 4/23 11:59pm |
|
| M 4/10 | HMMs & Particle Filtering | Ch. 15.2,6 | PPT & PDF | HW9 |
F 4/28 11:59pm |
|
| W 4/12 | ML: Naive Bayes | Ch. 15.2,6 | PPT & PDF |
(section 9 / exam-prep 7) |
||
| M 4/17 | ML: Perceptrons | PPT & PDF |
(section 10 / exam-prep 8) |
|||
| W 4/19 | ML: Deep Learning | PPT & PDF | P6: Classification | F 4/28, 5pm | ||
| M 4/24 | Advanced Topics | PPT & PDF | ||||
| W 4/26 | Advanced Topics / Final Contest | (section 11 / exam-prep 9) | ||||
| F 5/12 | FINAL EXAM (3-6pm) | |||||