The schedule below represents my current best estimate concerning due dates (and everything else). I am providing this information to give you a general idea of the pace and timing of the class. This schedule will certainly change as the semester progresses.
Unless otherwise noted, all readings are from
- Artificial Intelligence: Foundations of Computational Agents, 2nd Edition, David Poole and Alan Mackworth, Cambridge University Press, 2017.
| WEEK | DATE | TOPIC | READING/VIDEOS | OUT | IN |
| 1. Uninformed Search | 01-08 | Introduction/Syllabus | CH 1 | HW1, PA1 | |
| 01-10 | Uninformed Search (.pdf) Minimum Cost Search (.pdf) search_exercises.pdf |
CH 3-3.5.4, 3.7.1-3.7.2 Search Videos Berkeley Video (S) |
Reading Quiz Background Survey (Canvas) |
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| 01-12 | A* astar_activity.pdf |
CH 3.6, CH 3.7, 3.8.2 http://en.wikipedia.org/wiki/NP-complete A* Videos Berkeley Video (S) |
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| 2. Informed Search | 01-15 | MLK Day | HW1 | ||
| 01-17 | Snow Day | ||||
| 01-19 | Discuss Turing Paper | Computing Machinery and Intelligence | |||
| 3. Adversarial Search | 01-22 | Minimax (.pdf) minimax_activity.pdf |
CH 11.2-11.2.2,11.3 Minimax Videos Berkeley Video (S) |
Poster Project |
PA1 |
| 01-24 | Expectimax + Evaluation Functions MIT AI:6.034 Games and Utility Handout (Exercises 1 and 3) |
CH 8.1.4 (s) Evaluation Function Video (Through 37:00) Expectimax Video (First 19min) |
PA2 | ||
| 01-26 | Constraint Satisfaction Problems | CH 4-4.4 Skim CH 4.5-4.6 CSP Videos git tutorial Github tutorial |
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| 4. Logic | 01-29 | Propositional Logic Exercises (.pdf) | CH 5-5.1, 5.3 Propositional Logic Videos |
PA2A | |
| 01-31 | resolution_exercises.pdf |
Propositional Resolution Chapter Propositional Resolution Videos |
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| 02-02 | Predicate Logic pred_logic.pdf |
CH 13-13.3.1 Predicate Logic Video (Through 16:00) Predicate Logic Review (section 4) (s) |
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| 5. Midterm | 02-05 | Inference in Predicate Logic pred_logic_proofs.pdf |
CH 13.3.2-13.4 Resolution Notes (8.3-8.4) Videos |
PA2 | |
| 02-07 | (Snow day) Linear Algebra Linear Algebra (.pdf) linear_algebra_exercises.pdf |
Linear Algebra Review and Reference (.pdf) sections 1-3.7, (skip 3.6) Linear Algebra Video |
HW2 | ||
| 02-09 | Midterm Review | ||||
| 6. Machine Learning | 02-12 | Midterm #1 | PA3 | ||
| 02-14 | Gradient Descent +Linear Regression (.pdf) gradient_descent_exercises.pdf |
CH 4.9.2, 7-7.2.1 CH 7.3.2 (skim) Calculus Refresher (.pdf) (9-14) (s) Videos |
HW2 (9:00 AM) | ||
| 02-16 | Numpy exercises (.pdf) numpy_exercises.py |
Numpy Tutorial (only Numpy Section) | |||
| 7. Machine Learning | 02-19 | Classification Classification (.pdf) single_layer_network.py usps.npy |
CH 7.3.2 Videos |
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| 02-21 | Multi-layer Neural Networks (.pdf) | CH 7.5 Videos |
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| 02-23 | Convolutional Neural Networks (.pdf) Convolutional NN activity (.pdf) |
CNN Tutorial | |||
| 8. Machine Learning | 02-26 | Validation K-Nearest Neighbors Validation (.pdf) K-Nearest Neighbors (.pdf) |
7.4 (skip 7.4.1) 7.7 Videos |
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| 02-28 | Decision Trees Decision Tree Activity (.pdf) |
7.3.1 Video |
PA3 | ||
| 03-02 | Ensemble Method Activity | CH 7.6 Videos |
Poster Proposal | ||
| 9. Spring Break |
03-05 | SPRING BREAK | |||
| 03-07 | SPRING BREAK | ||||
| 03-09 | SPRING BREAK | ||||
| 10. Probabilistic AI | 03-12 | Start PA4 | PA4 | ||
| 03-14 | Support Vector Machines Supervised Learning Wrap |
SVM Tutorial | |||
| 03-16 | Intro Probability Intro Probability Probability Activity (.pdf) |
CH 8.1-8.2 Videos |
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| 11. Probabilistic AI | 03-19 | Belief Nets Naive Bayes Classifier Belief Nets |
Video (s) CH 8.3, 10.1.2 |
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| 03-21 | Inference in Belief Nets (Snow Day) |
Videos (s) CH 8.4 (s) |
HW3 | ||
| 03-23 | Markov Models (.pdf) Markov Model Activity (.pdf) |
CH 8.5-8.5.2 Video |
HW3 (11:00PM) | ||
| 12. Midterm | 03-26 | Hidden Markov Models filtering.pdf |
CH 8.5.3-8.5.5 CH 8.5.6 (s) Videos |
PA4 | |
| 03-28 | Review | Poster Bibliography | |||
| 03-30 | EXAM 2 |
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| 13. Reinforcement Learning | 04-02 | Markov Decision Processes mdp_activity.pdf |
CH 9.5-9.5.3 Video |
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| 04-04 | Q-Learning q_learning_activity.pdf |
CH 12-12.5 Video (first 17 min at least) |
PA5 | ||
| 04-06 | Approximate Q-Learning approx_rl_activity.pdf |
Video | |||
| 14. Unsupervised Learning | 04-09 | Genetic Algorithms Genetic Algorithms (.pdf) GA Demo |
CH 4.7-4.8 Genetic Algorithms (.pdf) |
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| 04-11 | K-Means Clustering K-Means Activity |
CH 10.2-10.2.1 Video |
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| 04-13 | Normal Distribution Expectation Maximization |
Multivariate Statistics Tutorial 6.5.1, 6.5.4-6.5.4.2 Videos |
Poster Submission | ||
| 15. Poster Sessions | 04-16 | Work Day | |||
| 04-18 | Ethics and AI | The Ethics of Artificial Intelligence (.pdf) Videos |
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| 04-20 | POSTER SESSION | ||||
| 16. Ethics and Review | 04-23 | POSTER SESSION | |||
| 04-25 | Review | PA5 |