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====== Schedule ====== | ====== Schedule ====== | ||
- | /* | ||
- | Follow this link to view all [[https:// | ||
- | 115b6-e68b-4318-89ff-d1ecf025c0b9|lecture videos]]. | ||
- | */ | ||
===== Announcements ===== | ===== Announcements ===== | ||
- | **Feb 27:** In the Schedule next to the A2 assignment you will find a link to good examples of reports submitted for A2. | + | Lecture videos are available from the Canvas site (in the menu on the left) by selecting [[https:// |
- | Lecture videos | + | /* |
+ | are available at this [[https:// | ||
+ | */ | ||
- | ===== January | + | ===== August |
|< 100% 10% 20% 30% 20% 20% >| | |< 100% 10% 20% 30% 20% 20% >| | ||
^ Week ^ Topic ^ Material | ^ Week ^ Topic ^ Material | ||
- | | Week 1: | + | | Week 1: |
- | | Week 2:\\ Jan 23 - Jan 27 | + | | Week 2:\\ Aug 28 - Sept 1 |
- | ===== February | + | ===== September |
|< 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. | + | | Week 3:\\ Sept 4 - Sept 8 |
- | | Week 4:\\ Feb 6 - Feb 10 | Regression with fixed nonlinearities. Nonlinear regression with neural networks.\\ Feb 10: Guest Speaker [[https:// | + | | Week 4:\\ Sept 11 - Sept 15 | A* optimality, admissible heuristics, effective branching factor.\\ Local search and optimization. | | Chapter 4 |
- | | Week 5:\\ Feb 13 - Feb 17 | Neural Networks | + | | Week 5:\\ Sept 18 - Sept 22 | Adversarial search. Minimax. Alpha-beta pruning. Negamax, |
- | | Week 6:\\ Feb 20 - Feb 24 | Neural Networks. Autoencoders. Guest lectures by our GTA, Jake Lee. | [[http:// | + | | Week 6:\\ Sept 25 - Sept 29 | Stochastic games. Expectimax. | |
- | | Week 7:\\ Feb 27 - Mar 3 | Recurrent Neural Networks.\\ Conditional probabilities and Bayes Rule | [[http:// | + | |
- | ===== March ===== | + | ===== October |
|< 100% 10% 20% 30% 20% 20% >| | |< 100% 10% 20% 30% 20% 20% >| | ||
^ Week ^ Topic ^ Material | ^ Week ^ Topic ^ Material | ||
- | | Week 8:\\ Mar 6 - Mar 10 | Classification. LDA and QDA. Linear and Nonlinear Logistic Regression. | [[http://nbviewer.ipython.org/ | + | | Week 7:\\ Oct 2 - Oct 6 | Introduction to Reinforcement Learning. | | Chapter 21\\ [[http://webdocs.cs.ualberta.ca/~sutton/book/the-book.html|Reinforcement Learning: An Introduction]]\\ [[http:// |
- | | Week 9:\\ Mar 20, Mar 24\\ <color red>No class March 22nd.</ | + | | Week 8:\\ Oct 9 - Oct 13 |
- | | Week 10:\\ Mar 27 - Mar 31 | + | | Week 9:\\ Oct 16 - Oct 20 |
+ | | Week 10:\\ Oct 23 - Oct 27 | | | ||
- | ===== April ===== | + | ===== November |
|< 100% 10% 20% 30% 20% 20% >| | |< 100% 10% 20% 30% 20% 20% >| | ||
^ Week ^ Topic ^ Material | ^ Week ^ Topic ^ Material | ||
- | | Week 11:\\ Apr 3 - Apr 7 | | | + | | Week 11:\\ Oct 30 - Nov 3 |
- | | Week 12:\\ Apr 10 - Apr 14 | | + | | Week 12:\\ Nov 6 - Nov 10 | | | | |
- | | Week 13:\\ Apr 17 - Apr 21 | + | | Week 13:\\ Nov 13 - Nov 17 |
- | | Week 14:\\ Apr 24 - Apr 28 | | + | | Nov 20 - Nov 24 | Fall Break | |
+ | | Week 14:\\ Nov 27 - Dec 1 | ||
- | ===== May ===== | + | ===== December |
|< 100% 10% 20% 30% 20% 20% >| | |< 100% 10% 20% 30% 20% 20% >| | ||
^ Week ^ Topic ^ Material | ^ Week ^ Topic ^ Material | ||
- | | Week 15:\\ May 1 - May 5 | | + | | Week 15:\\ Dec 4 - Dec 8 |
+ | | Finals Week:\\ Dec 11 - Dec 15 | | | | | ||