schedule-past

# Differences

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 — schedule-past [2018/01/20 17:06] (current) Line 1: Line 1: + + + + /* + ===== January ===== + + |< 100% 10% 20% 30% 20% 20%  >| + ^  Week      ^  Topic      ^  Material ​ ^  Reading ​         ^  Assignments ​ ^ + | Week 1:\\  Jan 16 - Jan 19    | Overview. Intro to machine learning. Python. ​ | [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs480/​notebooks/​01 Course Overview.ipynb|01 Course Overview]],​\\ ​ [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs480/​notebooks/​02 Matrices and Plotting.ipynb|02 Matrices and Plotting]], ​ | [[http://​www.nytimes.com/​2016/​12/​14/​magazine/​the-great-ai-awakening.html?​_r=0|The Great A.I. Awakening]],​ by Gideon Lewis-Krause,​ NYT, Dec 14, 2016.\\ Section 1 of   ​[[http://​www.scipy-lectures.org|Scipy Lecture Notes]] ​     |  | + | Week 2:\\ Jan 23 - Jan 27    | Probability distributions and regression. ​   | [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs480/​notebooks/​03 Linear Regression.ipynb|03 Linear Regression]],​\\ [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs480/​notebooks/​04 Gaussian Distributions.ipynb|04 Gaussian Distributions]] ​   |   ​| ​ + + + ===== February ===== + + |< 100% 10% 20% 30% 20% 20%  >| + ^  Week      ^  Topic      ^  Material ​ ^  Reading ​         ^  Assignments ​ ^ + | Week 3:\\ Jan 30 - Feb 3      | Probabilistic Linear Regression. Ridge regression. Data partitioning. On-line, incremental regression. ​ | [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs480/​notebooks/​05 Fitting Gaussians.ipynb|05 Fitting Gaussians]],​\\ [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs480/​notebooks/​06 Probabilistic Linear Regression.ipynb|06 Probabilistic Linear Regression]],​\\ [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs480/​notebooks/​07 Linear Ridge Regression and Data Partitioning.ipynb|07 Linear Ridge Regression and Data Partitioning]],​\\ [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs480/​notebooks/​08 Sample-by-Sample Linear Regression.ipynb|08 Sample-by-Sample Linear Regression]] ​   | | [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs480/​notebooks/​A1 Linear Regression.ipynb|A1 Linear Regression]] due Monday, January 30th at 10:00 PM.   ​| ​ + | Week 4:\\ Feb 6 - Feb 10   | Regression with fixed nonlinearities. Nonlinear regression with neural networks.\\ Feb 10: Guest Speaker [[https://​www.linkedin.com/​in/​mike-morain-07223710|Mike Morain]], Machine Learning at Amazon, UK.  | [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs480/​notebooks/​09 Linear Regression with Fixed Nonlinear Features.ipynb|09 Linear Regression with Fixed Nonlinear Features]],​\\ [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs480/​notebooks/​10 Nonlinear Regression with Neural Networks.ipynb|10 Nonlinear Regression with Neural Networks]] ​  | |   ​| ​ + | Week 5:\\ Feb 13 - Feb 17   | Neural Networks ​ | [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs480/​notebooks/​10 Nonlinear Regression with Neural Networks.ipynb|10 Nonlinear Regression with Neural Networks]],​\\ ​ [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs480/​notebooks/​11 More Nonlinear Regression with Neural Networks.ipynb|11 More Nonlinear Regression with Neural Networks]] ​  | | [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs480/​notebooks/​A2 Ridge Regression with K-Fold Cross-Validation.ipynb|A2 Ridge Regression with K-Fold Cross-Validation]] due Monday, February 13th at 10:00 PM.\\ Here are [[A2-good-ones|examples of good A2 reports.]] ​ | + | Week 6:\\ Feb 20 - Feb 24   | Neural Networks. Autoencoders. Guest lectures by our GTA, Jake Lee.  | [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs480/​notebooks/​12 Autoencoder Neural Networks.ipynb|12 Autoencoder Neural Networks]] ​  | |   ​| ​   ​ + | Week 7:\\ Feb 27 - Mar 3   | Recurrent Neural Networks.\\ Conditional probabilities and Bayes Rule  | [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs480/​notebooks/​13 Recurrent Neural Networks.ipynb|13 Recurrent Neural Networks]]\\ [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs480/​notebooks/​14 Introduction to Classification.ipynb|14 Introduction to Classification]] ​  | | [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs480/​notebooks/​A3 Neural Network Regression.ipynb|A3 Neural Network Regression]] due Wednesday, March 1st at 10:00 PM.\\ Here are [[A3-good-ones|examples of good A3 reports.]] ​ |    ​ + + ===== March ===== + + |< 100% 10% 20% 30% 20% 20%  >| + ^  Week      ^  Topic      ^  Material ​ ^  Reading ​         ^  Assignments ​ ^ + | Week 8:\\ Mar 6 - Mar 10   | Classification. LDA and QDA. Linear and Nonlinear Logistic Regression. ​ | [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs480/​notebooks/​15 Classification with Linear Logistic Regression.ipynb|15 Classification with Linear Logistic Regression]]\\ [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs480/​notebooks/​16 Classification with Nonlinear Logistic Regression Using Neural Networks.ipynb|16 Classification with Nonlinear Logistic Regression Using Neural Networks]] ​  | |  | + | Week 9:\\ Mar 20, Mar 24\\ No class March 22nd. ​ | Classification. Analysis of Trained Networks. Bottleneck Networks. Hand-Drawn Digit Classification. ​ | [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs480/​notebooks/​17 Analysis of Neural Network Classifiers and Bottleneck Networks.ipynb|17 Analysis of Neural Network Classifiers and Bottleneck Networks]]\\ [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs480/​notebooks/​18 Digits.ipynb|18 Digits]] ​ |  |  | + | Week 10:\\ Mar 27 - Mar 31  | Convolutional Neural Networks. Reinforcement Learning. ​ | [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs480/​notebooks/​19 Convolutional Neural Networks.ipynb|19 Convolutional Neural Networks]]\\ [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs480/​notebooks/​20 Introduction to Reinforcement Learning.ipynb|20 Introduction to Reinforcement Learning]] ​ | [[http://​incompleteideas.net/​sutton/​book/​the-book-2nd.html| Reinforcement Learning: An Introduction]],​ by Richard Sutton and Andrew Barto. 2nd edition draft. On-line and free.  |  | + + ===== April ===== + + |< 100% 10% 20% 30% 20% 20%  >| + ^  Week      ^  Topic      ^  Material ​ ^  Reading ​         ^  Assignments ​ ^ + | Week 11:\\ Apr 3 - Apr 7   | Reinforcement Learning. ​ Two-player games. ​ | [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs480/​notebooks/​21 Reinforcement Learning for Two Player Games.ipynb|21 Reinforcement Learning for Two Player Games]] ​  | [[http://​incompleteideas.net/​sutton/​book/​the-book-2nd.html| Reinforcement Learning: An Introduction]],​ by Richard Sutton and Andrew Barto. 2nd edition draft. On-line and free.  |  [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs480/​notebooks/​A4 Classification with LDA and Logistic Regression.ipynb|A4 Classification with LDA and Logistic Regression]] due Wednesday, April 5th at 10:00 PM.\\ Here are [[A4-good-ones|examples of good A4 reports.]]\\ [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs480/​notebooks/​Project Proposal.ipynb|Project Proposal]] due Friday, April 7th at 10:00 PM.  | + | Week 12:\\ Apr 10 - Apr 14  | Neural networks as Q functions. ​ | [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs480/​notebooks/​22 Reinforcement Learning with Neural Network as Q Function.ipynb|22 Reinforcement Learning with Neural Network as Q Function]]\\ [[http://​www.cs.colostate.edu/​~anderson/​cs480/​notebooks/​17pole.odp|Faster RL by Pre-training]] ​ | [[https://​www.technologyreview.com/​s/​604087/​the-dark-secret-at-the-heart-of-ai/​|The Dark Secret at the Heart of AI]]\\ [[https://​flipboard.com/​@flipboard/​flip.it%2FVaiyLS-the-tiny-changes-that-can-cause-ai-to-f/​f-32bef81237%2Fbbc.com|The Tiny Changes That Can Cause AI to Fail]] ​ |  | + | Week 13:\\ Apr 17 - Apr 21  | Unsupervised Learning. Dimensionality reduction. ​ | [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs480/​notebooks/​23 Linear Dimensionality Reduction.ipynb|23 Linear Dimensionality Reduction]]\\ ​ [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs480/​notebooks/​24 Nonlinear Dimensionality Reduction with Digits Example.ipynb|24 Nonlinear Dimensionality Reduction with Digits Example]]\\ [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs480/​notebooks/​25 K-Means Clustering.ipynb|25 K-Means Clustering]]\\ [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs480/​notebooks/​26 Hierarchical Clustering.ipynb|26 Hierarchical Clustering]] ​  ​| ​ |  | + | Week 14:\\ Apr 24 - Apr 28  | Nonparametric Classification Algorithms ​ | [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs480/​notebooks/​27 Nonparametric Classification with K Nearest Neighbors.ipynb|27 Nonparametric Classification with K Nearest Neighbors]]\\ [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs480/​notebooks/​28 Support Vector Machines.ipynb|28 Support Vector Machines]] ​ |  | [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs480/​notebooks/​A5 Control a Marble with Reinforcement Learning.ipynb|A5 Control a Marble with Reinforcement Learning]] due Monday, April 24th at 10:00 PM.\\ Here are [[A5-good-ones|examples of good A5 reports.]] | + + ===== May ===== + + |< 100% 10% 20% 30% 20% 20%  >| + ^  Week      ^  Topic      ^  Material ​ ^  Reading ​         ^  Assignments ​ ^ + | Week 15:\\ May 1 - May 5   | Brain-Computer Interfaces. ​ Ensembles. ​  | [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs480/​notebooks/​29 Machine Learning for Brain-Computer Interfaces.ipynb|29 Machine Learning for Brain-Computer Interfaces]]\\ [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs480/​notebooks/​30 Comparison of Algorithms for BCI.ipynb|30 Comparison of Algorithms for BCI]]\\ [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs480/​notebooks/​31 Convolutional Neural Networks for BCI.ipynb|31 Convolutional Neural Networks for BCI]]\\ [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs480/​notebooks/​32 Ensembles of Convolutional Neural Networks.ipynb|32 Ensembles of Convolutional Neural Networks]]\\ [[http://​www.cs.colostate.edu/​~anderson/​cs480/​notebooks/​16harvard.odp|Patterns in EEG for Brain-Computer Interfaces and Recent Results with Tripolar EEG Electrodes]] ​  ​| ​ | Please complete the Course Surveys that are now available on Canvas. ​ Fill out the survey for your section, either on-campus or distance-learning. ​ | + | Finals Week:\\ May 8 - May 11  |   ​| ​ |  |  Final project due Tuesday, May 9, 10:00 PM.   ​[[Final Project Report|Here is a summary]] of what is expected in your reports. ​  | + + */