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- | ====== Schedule ====== | + | ===== Schedule ====== |
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- | Follow this link to view all [[https:// | + | |
===== Announcements ===== | ===== Announcements ===== | ||
- | **May 9:** At the bottom of this page is a link to a summary of the content expected in your project reports. | + | Links to live MS Teams events: |
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- | **April 29:** My latest neural network code is available | + | Recordings of lecture and office hour videos are available |
+ | [[https://colostate.instructure.com/courses/109411|Canvas site]]. | ||
- | ===== January ===== | + | To use jupyter notebooks on our CS department machines, you must add this line to your .bashrc file: |
- | |< 100% 20% 20% 30% 10% 20% >| | + | |
- | ^ Week ^ Topic ^ Material | + | |
- | | Week 1:\\ Jan 19 - Jan 22 | Overview. Intro to machine learning. Python. | + | |
- | | Week 2:\\ Jan 25 - Jan 29 | Probability distributions and regression. | + | |
- | ===== February ===== | + | This is a tentative schedule of CS440 topics for Fall, 2020. This will be updated during the summer and as the fall semester continues. |
- | |< 100% 20% 20% 30% 10% 20% >| | ||
- | ^ Week ^ Topic ^ Material | ||
- | | Week 3:\\ Feb 1 - Feb 5 | Ridge regression. Data partitioning. On-line, incremental regression. Regression with fixed nonlinearities. | ||
- | | Week 4:\\ Feb 8 - Feb 12 | Nonlinear regression with neural networks. | ||
- | | Week 5:\\ Feb 15 - Feb 19 | Autoencoders. Recurrent neural networks. | ||
- | | Week 6:\\ Feb 22 - Feb 26 | Classification, | ||
- | ===== March ===== | + | ===== August |
- | |< 100% 20% 20% 30% 10% 20% >| | + | |< 100% 18% 20% 22% 20% 20% >| |
^ Week ^ Topic ^ Material | ^ Week ^ Topic ^ Material | ||
- | | Week 7:\\ Feb 29 - Mar 5 | Classification, | + | | Week 1:\\ Aug 24 - Aug 28 |
- | | Week 8:\\ Mar 7 - Mar 11 | Classification with neural networks. | + | |
- | | Mar 14 - Mar 18 | Spring Break! | + | |
- | | Week 9:\\ Mar 21 - Mar 25 | Bottleneck, and deep networks. | [[http:// | + | |
- | | Week 10:\\ Mar 28 - Apr 1 | Convolutional neural nets. Clustering. | + | |
- | ===== April ===== | + | ===== September |
- | |< 100% 20% 20% 30% 10% 20% >| | + | |< 100% 18% 20% 22% 20% 20% > |
+ | ^ 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 3:\\ Sept 7 - Sept 11 | Informed search. A* search. Python classes, sorting, numpy arrays. | ||
+ | | Week 4:\\ Sept 14 - Sept 18 | A* optimality, admissible heuristics | ||
+ | | Week 5:\\ Sept 21 - Sept 25 | Effective branching factor.\\ Local search and optimization. Adversarial search. Minimax. Alpha-beta pruning. Stochastic games. | ||
+ | | Week 6:\\ Sept 28 - Oct 2 | Negamax, with pruning. Introduction to Reinforcement Learning. | ||
+ | |||
+ | ===== October ===== | ||
+ | |||
+ | |< 100% 18% 20% 22% 20% 20% >| | ||
^ Week ^ Topic ^ Material | ^ Week ^ Topic ^ Material | ||
- | | Week 11:\\ Apr 4 - Apr 8 | Reinforcement Learning | + | | Week 7:\\ Oct 5 - Oct 9\\ <color red> |
- | | Week 12:\\ Apr 11 - Apr 15 | + | | Week 8:\\ Oct 12 - Oct 16 |
- | | Week 13:\\ Apr 18 - Apr 22 | Nonparametric methods | + | | Week 9:\\ Oct 19 - Oct 23 | Natural language processing. |
- | | Week 14:\\ Apr 25 - Apr 29 | | [[http://nbviewer.ipython.org/ | + | | Week 10:\\ Oct 26 - Oct 30 | Introduction to Neural Networks |
+ | ===== November ===== | ||
- | ===== May ===== | + | |< 100% 18% 20% 22% 20% 20% >| |
+ | ^ Week ^ Topic ^ Material | ||
+ | | Week 11:\\ Nov 2 - Nov 6 | More Neural Networks | ||
+ | | Week 12:\\ Nov 9 - Nov 13 | Interpreting what a neural network has learned. | ||
+ | | Week 13:\\ Nov 16 - Nov 20 | Natural language processing with neural nets. | [[https:// | ||
+ | | Nov 23 - Nov 27 | Fall Recess! | ||
- | |< 100% 20% 20% 30% 10% 20% >| | + | ===== December ===== |
+ | |||
+ | |< 100% 18% 20% 22% 20% 20% >| | ||
^ Week ^ Topic ^ Material | ^ Week ^ Topic ^ Material | ||
- | | Week 15:\\ May 2 - May 6 | + | | Week 14:\\ Nov 30 - Dec 4 |
+ | | Week 15:\\ Dec 7 - Dec 11 | Brain-Computer Interfaces. Pre-training for faster reinforcement learning. | ||
+ | | Final Exam Week:\\ Dec 14 - Dec 18 | No exam. | | | [[https:// | ||
- | | Week 16:\\ May 10 | Final Project Notebook Due. | | | Check in final project notebook by Tuesday, May 10th, at 10:00 PM. [[Final Project Report|Here is a summary]] of what is expected in your reportsl | ||
- | Selected Project Reports (in no particular order): | ||
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