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====== Schedule ====== | ====== Schedule ====== | ||
+ | |||
+ | ===== Announcements ===== | ||
+ | |||
+ | Sept 7: Assignment 2 is now complete. | ||
+ | |||
+ | Aug 31: Assignment 1 now includes another example. | ||
+ | |||
+ | |||
+ | Lecture videos are available from the Canvas site (in the menu on the left) by selecting [[https:// | ||
+ | |||
+ | To use jupyter notebooks on our CS department machines, you must add this line to your .bashrc file: | ||
+ | |||
+ | export PATH=/ | ||
/* | /* | ||
- | Follow | + | are available at this [[https:// |
- | 115b6-e68b-4318-89ff-d1ecf025c0b9|lecture videos]]. | + | |
*/ | */ | ||
- | ===== Announcements ===== | ||
- | **January 25:** For Assignment 1 you must standardize the data in X. An update has been added to the assignment description. | ||
- | **January 20:** Assignment 1 (A1) is now due Monday, January 30th, at 10 PM. | + | ===== August ===== |
- | Lecture videos are available at this [[https://echo.colostate.edu/ | + | |< 100% 10% 20% 30% 20% 20% >| |
+ | ^ Week ^ Topic ^ Material | ||
+ | | Week 1:\\ Aug 21 - Aug 25 | What is AI? Promises and fears.\\ Python review.\\ Problem-Solving Agents. | ||
+ | | Week 2:\\ Aug 28 - Sept 1 | Problem-solving search and how to measure performance.\\ Iterative deepening and other uninformed search methods. | ||
- | ===== January | + | ===== September |
|< 100% 10% 20% 30% 20% 20% >| | |< 100% 10% 20% 30% 20% 20% >| | ||
^ Week ^ Topic ^ Material | ^ Week ^ Topic ^ Material | ||
- | | Week 1:\\ Jan 17 - Jan 20 | + | | Week 3:\\ Sept 4 - Sept 8 |
- | | Week 2:\\ Jan 23 - Jan 27 | + | | Week 4:\\ Sept 11 - Sept 15 | A* optimality, admissible heuristics, effective branching factor.\\ Local search and optimization. |
+ | | Week 5:\\ Sept 18 - Sept 22 | Adversarial search. Minimax. Alpha-beta pruning. Negamax, with pruning. | ||
+ | | Week 6:\\ Sept 25 - Sept 29 | Stochastic games. Expectimax. | | Sections 5.5 - 5.6 | [[http:// | ||
+ | ===== October ===== | ||
- | ===== February | + | |< 100% 10% 20% 30% 20% 20% >| |
+ | ^ Week ^ Topic ^ Material | ||
+ | | Week 7:\\ Oct 2 - Oct 6 | | ||
+ | | Week 8:\\ Oct 9 - Oct 13 | | ||
+ | | Week 9:\\ Oct 16 - Oct 20 | | ||
+ | | Week 10:\\ Oct 23 - Oct 27 | Introduction to Reinforcement Learning. | ||
+ | |||
+ | ===== November | ||
|< 100% 10% 20% 30% 20% 20% >| | |< 100% 10% 20% 30% 20% 20% >| | ||
^ Week ^ Topic ^ Material | ^ Week ^ Topic ^ Material | ||
- | | Week 3:\\ Jan 30 - Feb 3 | Probabilistic Linear Regression. Ridge regression. Data partitioning. On-line, incremental regression. Regression with fixed nonlinearities. | + | | Week 11:\\ Oct 30 - Nov 3 |
+ | | Week 12:\\ Nov 6 - Nov 10 | | | | | ||
+ | | Week 13:\\ Nov 13 - Nov 17 | | | | | ||
+ | | Nov 20 - Nov 24 | Fall Break | | ||
+ | | Week 14:\\ Nov 27 - Dec 1 | Constraint satisfaction. Min-conflicts | ||
+ | |||
+ | ===== December ===== | ||
+ | |||
+ | |< 100% 10% 20% 30% 20% 20% >| | ||
+ | ^ Week ^ Topic ^ Material | ||
+ | | Week 15:\\ Dec 4 - Dec 8 | Propositional and First-Order Logic. Introduction to Prolog. | | Chapters 7, 8, 9 | | ||
+ | | Finals Week:\\ Dec 11 - Dec 15 | ||