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====== Schedule ====== | ====== Schedule ====== | ||
- | /* | ||
- | Follow this link to view all [[https:// | ||
- | 115b6-e68b-4318-89ff-d1ecf025c0b9|lecture videos]]. | ||
- | */ | ||
===== Announcements ===== | ===== Announcements ===== | ||
- | **Feb 28:** In A3, my sample output had incorrect validation errors. | ||
- | **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:// | + | To use jupyter notebooks on our CS department machines, you must add this line to your .bashrc file: |
+ | export PATH=/ | ||
- | ===== January | + | |
+ | This is a tentative schedule of CS440 topics for Fall, 2018. This will be updated during the summer and as the fall semester continues. | ||
+ | |||
+ | |||
+ | ===== 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 | Probability distributions | + | | Week 2:\\ Aug 27 - Aug 31 |
- | ===== 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. | [[http:// | + | | Week 3:\\ Sept 4 - Sept 7\\ No class on the Sept 3 University Holiday |
- | | Week 4:\\ Feb 6 - Feb 10 | Regression | + | | Week 4:\\ Sept 10 - Sept 14 | A* optimality, admissible heuristics, effective branching factor.\\ Local search and optimization. |
- | | Week 5:\\ Feb 13 - Feb 17 | Neural Networks | + | | Week 5:\\ Sept 17 - Sept 21 | Adversarial search. Minimax. Alpha-beta pruning. Stochastic games. |
- | | Week 6:\\ Feb 20 - Feb 24 | Neural Networks. Autoencoders. Guest lectures by our GTA, Jake Lee. | [[http:// | + | | Week 6:\\ Sept 24 - Sept 28 | Negamax, |
- | | Week 7:\\ Feb 27 - Mar 3 | Recurrent Neural Networks. | [[http:// | + | |
+ | ===== October ===== | ||
+ | |||
+ | |< 100% 10% 20% 30% 20% 20% >| | ||
+ | ^ Week ^ Topic ^ Material | ||
+ | | Week 7:\\ Oct 2 - Oct 6 | Introduction to Reinforcement Learning. | ||
+ | | Week 8:\\ Oct 9 - Oct 13 | Reinforcement | ||
+ | | Week 9:\\ Oct 16 - Oct 20 | ||
+ | | Week 10:\\ Oct 23 - Oct 27 | Introduction to Classification. Bayes Rule. Generative versus Discriminative. Linear Logistic | ||
+ | |||
+ | ===== November ===== | ||
+ | |||
+ | |< 100% 10% 20% 30% 20% 20% >| | ||
+ | ^ Week ^ Topic ^ Material | ||
+ | | Week 11:\\ Oct 30 - Nov 2 | Classification | ||
+ | | Week 12:\\ Nov 5 - Nov 9 | ||
+ | | Week 13:\\ Nov 12 - Nov 16 | ||
+ | | Nov 19 - Nov 23 | Fall Recess | ||
+ | | Week 14:\\ Nov 26 - Nov 30 | Constraint satisfaction. Min-conflicts | ||
+ | |||
+ | ===== December ===== | ||
+ | |||
+ | |< 100% 10% 20% 30% 20% 20% >| | ||
+ | ^ Week ^ Topic ^ Material | ||
+ | | Week 15:\\ Dec 3 - Dec 7 | Recurrent neural networks and use in natural language | ||
+ | | Final Exam Week:\\ Dec 10 - Dec 14 | | ||