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===== Announcements ===== | ===== Announcements ===== | ||
- | **January 25:** For Assignment 1 you must standardize the data in X. An update has been added to the assignment description. | + | **Feb 19:** The links in A3 for nnA3.tar and A3grader.tar now work. |
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- | **January 20:** Assignment 1 (A1) is now due Monday, January 30th, at 10 PM. | + | |
Lecture videos are available at this [[https:// | Lecture videos are available at this [[https:// | ||
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^ 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. | [[http:// | + | | Week 3:\\ Jan 30 - Feb 3 | Probabilistic Linear Regression. Ridge regression. Data partitioning. On-line, incremental regression. |
- | /* \\ Here are five examples of good solutions: [[http:// | + | | Week 4:\\ Feb 6 - Feb 10 |
- | */ | + | | Week 5:\\ Feb 13 - Feb 17 |
- | + | | Week 6:\\ Feb 20 - Feb 24 | Neural Networks. Autoencoders. Guest lectures by our GTA, Jake Lee. | [[http:// | |
- | /* | + | | Week 7:\\ Feb 27 - Mar 3 | Recurrent |
- | |< 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, | + | |
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- | ===== March ===== | + | |
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- | |< 100% 20% 20% 30% 10% 20% >| | + | |
- | ^ Week ^ Topic ^ Material | + | |
- | | Week 7:\\ Feb 29 - Mar 5 | Classification, | + | |
- | | 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. | + | |
- | | Week 10:\\ Mar 28 - Apr 1 | Convolutional neural nets. Clustering. | + | |
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- | ===== April ===== | + | |
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- | |< 100% 20% 20% 30% 10% 20% >| | + | |
- | ^ Week ^ Topic ^ Material | + | |
- | | Week 11:\\ Apr 4 - Apr 8 | Reinforcement Learning | + | |
- | | Week 12:\\ Apr 11 - Apr 15 | Dimensionality reduction. | + | |
- | | Week 13:\\ Apr 18 - Apr 22 | Nonparametric methods | + | |
- | | Week 14:\\ Apr 25 - Apr 29 | + | |
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- | ===== May ===== | + | |
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- | |< 100% 20% 20% 30% 10% 20% >| | + | |
- | ^ Week ^ Topic ^ Material | + | |
- | | Week 15:\\ May 2 - May 6 | Multiple models.\\ PLEASE ATTEND MAY 6th LECTURE TO FILL OUT THE ASCSU STUDENT COURSE SURVEYS! Distance-section students will be filling out the survey on-line. | + | |
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- | | 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 | + | |
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- | Selected Project Reports (in no particular order): | + | |
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- | * [[http:// | + | |
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- | * [[http://nbviewer.ipython.org/ | + | |
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- | */ | ||