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- | ====== Schedule ====== | + | ===== Schedule ====== |
===== Announcements ===== | ===== Announcements ===== | ||
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^ Week ^ Topic ^ Material | ^ Week ^ Topic ^ Material | ||
- | | Week 1:\\ Aug 24 - Aug 28 | What is AI? Promises and fears.\\ Python review.\\ Problem-Solving Agents. | + | | Week 1:\\ Aug 24 - Aug 28 | What is AI? Promises and fears.\\ Python review.\\ Problem-Solving Agents. |
===== September ===== | ===== September ===== | ||
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^ Week ^ Topic ^ Material | ^ Week ^ Topic ^ Material | ||
- | | Week 2:\\ Aug 31 - Sept 4 | Help with A1.\\ Problem-solving search and how to measure performance.\\ Iterative deepening and other uninformed search methods. | + | | Week 2:\\ Aug 31 - Sept 4 | Help with A1.\\ Problem-solving search and how to measure performance.\\ Iterative deepening and other uninformed search methods. |
- | | Week 3:\\ Sept 7 - Sept 11 | Informed search. A* search. Python | + | | Week 3:\\ Sept 7 - Sept 11 | Informed search. A* search. Python classes, sorting, numpy arrays. |
- | | Week 4:\\ Sept 14 - Sept 18 | A* optimality, admissible heuristics, effective branching factor.\\ Local search and optimization. | + | | Week 4:\\ Sept 14 - Sept 18 | A* optimality, admissible heuristics |
- | | Week 5:\\ Sept 21 - Sept 25 | Adversarial search. Minimax. Alpha-beta pruning. Stochastic games. | + | | Week 5:\\ Sept 21 - Sept 25 |
- | | Week 6:\\ Sept 28 - Oct 2 | Negamax, with pruning. Introduction to Reinforcement Learning. | + | | Week 6:\\ Sept 28 - Oct 2 | Negamax, with pruning. Introduction to Reinforcement Learning. |
===== October ===== | ===== October ===== | ||
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^ Week ^ Topic ^ Material | ^ Week ^ Topic ^ Material | ||
- | | Week 7:\\ Oct 5 - Oct 9 | Reinforcement Learning for Two-Player Games. | + | | Week 7:\\ Oct 5 - Oct 9\\ <color red>Oct 8 Lecture will not meet, but recording will be available.</ |
- | | Week 8:\\ Oct 12 - Oct 16 | Introduction to Neural Networks | + | | Week 8:\\ Oct 12 - Oct 16 | Constraint satisfaction.\\ Min-conflicts. |
- | | Week 9:\\ Oct 19 - Oct 23 | More Neural Networks. Autoencoders. | + | | Week 9:\\ Oct 19 - Oct 23 | Natural language processing. |
- | | Week 10:\\ Oct 26 - Oct 30 | Introduction to Classification. Bayes Rule. Generative versus Discriminative. Linear Logistic Regression. | <!-- [[http://nbviewer.ipython.org/ | + | | Week 10:\\ Oct 26 - Oct 30 | Introduction to Neural Networks |
===== November ===== | ===== November ===== | ||
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^ Week ^ Topic ^ Material | ^ Week ^ Topic ^ Material | ||
- | | Week 11:\\ Nov 2 - Nov 6 | Classification with Neural Networks. Reinforcement Learning with Neural Networks. | + | | Week 11:\\ Nov 2 - Nov 6 | More Neural Networks |
- | | Week 12:\\ Nov 9 - Nov 13 | Introduction to Pytorch.\\ Constraint satisfaction.\\ Min-conflicts. | + | | Week 12:\\ Nov 9 - Nov 13 | Interpreting what a neural network has learned. |
- | | Week 13:\\ Nov 16 - Nov 20 | Natural language | + | | Week 13:\\ Nov 16 - Nov 20 | Natural language |
| Nov 23 - Nov 27 | Fall Recess! | | Nov 23 - Nov 27 | Fall Recess! | ||
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^ Week ^ Topic ^ Material | ^ Week ^ Topic ^ Material | ||
- | | Week 14:\\ Nov 30 - Dec 4 | Faster Reinforcement Learning | + | | Week 14:\\ Nov 30 - Dec 4 | Clustering. Word embeddings.\\ Genetic algorithms. |
- | | Week 15:\\ Dec 7 - Dec 11 | | <!-- **Dec 3:**\\ Tom Cavey: //Image Classification and Object Detection of Things Around CSU//\\ Jason Stock: // | + | | Week 15:\\ Dec 7 - Dec 11 | Brain-Computer Interfaces. Pre-training |
- | | Final Exam Week:\\ Dec 14 - Dec 18 | | + | | Final Exam Week:\\ Dec 14 - Dec 18 | No exam. |