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, David Poole and Alan Mackworth, Cambridge University Press, 2010.
| WEEK | DATE | TOPIC | READING/VIDEOS | OUT | IN |
| 1. Uninformed Search | 01-09 | Introduction/Syllabus | CH 1 | HW1 | |
| 01-11 | Uninformed Search Uninformed Search (.pdf) search_exercises.pdf |
CH 3-3.5.2, 3.7.1-3.7.2 Search Videos Berkeley Video (S) |
Reading Quiz Background Survey (Canvas) |
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| 01-13 | Complexity Theory Minimum Cost Search Complexity Theory Minimum Cost Exercises |
CH 3.5.3 http://en.wikipedia.org/wiki/NP-complete Videos |
PA1 | ||
| 2. Informed Search | 01-16 | MLK Day | HW1 | ||
| 01-18 | Discuss Turing Paper | Computing Machinery and Intelligence | |||
| 01-20 | Best First Search A* astar_activity.pdf |
CH 3.6, CH 3.7.3, 3.7.5 "Optimality of A*" A* Videos Berkeley Video (S) |
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| 3. Adversarial Search | 01-23 | Minimax (.pdf) minimax_activity.pdf |
CH 10.2-10.2.2,10.3 Minimax Videos Berkeley Video (S) |
PA2 Poster Project |
PA1 |
| 01-25 | Expectimax + Evaluation Functions MIT AI:6.034 Games and Utility Handout (Exercises 1 and 3) |
CH 6.1.4 (s) Evaluation Function Video (Through 37:00) Expectimax Video (First 19min) |
PA2 | ||
| 01-27 | Constraint Satisfaction Problems | CH 4-4.5 Skim CH 4.6-4.7 CSP Videos git tutorial Github tutorial |
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| 4. Propositional Logic | 01-30 | Propositional Logic | CH 5-5.2.2 Propositional Logic Videos |
PA2A | |
| 02-01 | Propositional Resolution resolution_exercises.pdf |
Propositional Resolution Chapter Propositional Resolution Videos |
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| 02-03 | Predicate Logic pred_logic.pdf |
CH 12-12.3.1 Predicate Logic Video (Through 16:00) Predicate Logic Review (section 4) (s) |
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| 5. Predicate Logic | 02-06 | Linear Algebra (.pdf) |
Linear Algebra Review and Reference (.pdf) sections 1-3.7, (skip 3.6) Linear Algebra Video |
PA2B | |
| 02-08 | Midterm Review | PA3 | |||
| 02-10 | Inference in Predicate Logic (No Class) pred_logic_proofs.pdf |
CH 12.3.2-12.4 Resolution Notes (8.3-8.4) Videos |
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| 6. Machine Learning | 02-13 | Midterm #1 | |||
| 02-15 | Gradient Descent +Linear Regression (.pdf) gradient_descent_exercises.pdf |
CH 4.10.2, 7-7.2.1, 7.3.2 Calculus Refresher (.pdf) (9-14) (s) Videos |
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| 02-17 | Numpy Numpy exercises (.pdf) numpy_exercises.py |
Numpy Tutorial (only Numpy Section) | |||
| 7. Machine Learning | 02-20 | Classification Classification (.pdf) single_layer_network.py usps.npy |
CH 7.3.2 Videos |
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| 02-22 | Multi-layer Neural Networks (.pdf) |
CH 7.4.1 Videos |
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| 02-24 | Convolutional Neural Networks (.pdf) | CNN Tutorial | |||
| 8. Machine Learning | 02-27 | Validation (.pdf) K-Nearest Neighbors (.pdf) |
7.5 (skip 7.5.1) 7.6 Videos |
PA3 | |
| 03-01 | Start PA4 | PA4 | |||
| 03-03 | Decision Tree Activity (.pdf) | 7.3.1, 7.4.2 Videos |
Poster Proposal | ||
| 9. Spring Break |
03-06 | SPRING BREAK | |||
| 03-08 | SPRING BREAK | ||||
| 03-10 | SPRING BREAK | ||||
| 10. Probabilistic AI | 03-13 | Kaggle | |||
| 03-15 | Support Vector Machines Supervised Learning Wrap |
Support Vector Machines: Hype or Halleluja? What is a Support Vector Machine? (s) |
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| 03-17 | Intro Probability Probability Activity (.pdf) |
CH 6.1-6.2 Videos |
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| 11. Probabilistic AI | 03-20 | Belief Nets | Video (s) CH 6.3, 7.3.3 |
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| 03-22 | Inference in Belief Nets Activity (.pdf) |
Videos (s) CH 6.4 (s) |
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| 03-24 | Markov Models (.pdf) Markov Model Activity (.pdf) |
CH 6.5-6.5.2 Video |
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| 12. Midterm | 03-27 | Hidden Markov Models filtering.pdf |
CH 6.5.3-6.6 Videos |
PA4 | |
| 03-29 | Review | Poster Bibliography | |||
| 03-31 | EXAM 2 |
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| 13. Genetic Algorithms | 04-03 | Markov Decision Processes mdp_activity.pdf |
CH 9.5-9.5.3 Video |
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| 04-05 | Q-Learning q_learning_activity.pdf |
CH 11.3-11.3.5 Video (first 17 min at least) |
PA5 | ||
| 04-07 | Approximate Q-Learning approx_rl_activity.pdf |
Video | |||
| 14. Unsupervised Learning | 04-10 | Genetic Algorithms (.pdf) GA Demo |
CH 4.8-4.8.2, 4.9 Genetic Algorithms (.pdf) |
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| 04-12 | K-Means Clustering K-Means Activity |
CH 11-11.1.2 Video |
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| 04-14 | Normal Distribution Expectation Maximization Gaussian Mixture Models |
Multivariate Statistics Tutorial 6.5.1, 6.5.4-6.5.4.2 Videos |
Poster Submission | ||
| 15. Poster Sessions | 04-17 | Work Day | |||
| 04-19 | Ethics and AI | The Ethics of Artificial Intelligence (.pdf) Humans Need Not Apply (video) |
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| 04-21 | POSTER SESSION | ||||
| 16. Ethics and Review | 04-24 | POSTER SESSION | |||
| 04-26 | Review | PA5 |