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start [2018/01/20 15:05]
anderson [January]
start [2018/02/15 17:19] (current)
anderson [February]
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 ====== Schedule ====== ====== Schedule ======
  
-/* 
-Follow this link to view all [[https://​echo.colostate.edu/​ess/​portal/​section/​37e 
-115b6-e68b-4318-89ff-d1ecf025c0b9|lecture videos]]. 
-*/ 
 ===== Announcements ===== ===== Announcements =====
  
-Lecture videos are available at this [[https://​colostate.instructure.com/​courses/​61937/​external_tools/​2755|CS480 video recordings site]].+**February 13:** Assignment A2 has been updated and now contains link to A2grader.tar. 
 + 
 +Lecture videos are available at this [[https://​colostate.instructure.com/​courses/​61937/​external_tools/​2755|CS445 video recordings site]].
  
  
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 |< 100% 10% 20% 30% 20% 20%  >| |< 100% 10% 20% 30% 20% 20%  >|
 ^  Week      ^  Topic      ^  Material ​ ^  Reading ​         ^  Assignments ​ ^ ^  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/​cs445/​notebooks/​01 Course Overview.ipynb|01 Course Overview]]\\ [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs445/​notebooks/​02 Matrices and Plotting.ipynb|02 Matrices and Plotting]]\\ [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs445/​notebooks/​03 Linear Regression.ipynb|03 Linear Regression]]  | [[http://​www.labri.fr/​perso/​nrougier/​from-python-to-numpy/​|From Python to Numpy]], Chapters 1-2\\ [[http://​www.deeplearningbook.org/​|Deep Learning]], Chapters 1-5.1.4 ​ | +| Week 1:\\  Jan 16 - Jan 19    | Overview. Intro to machine learning. Python. ​ | [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs445/​notebooks/​01 Course Overview.ipynb|01 Course Overview]]\\ [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs445/​notebooks/​02 Matrices and Plotting.ipynb|02 Matrices and Plotting]] ​ | [[http://​www.labri.fr/​perso/​nrougier/​from-python-to-numpy/​|From Python to Numpy]], Chapters 1 - 2\\ [[http://​www.deeplearningbook.org/​|Deep Learning]], Chapters 1 - 5.1.4  | 
-| Week 2:\\ Jan 22 - Jan 26    |  +| Week 2:\\ Jan 22 - Jan 26    ​| Fitting linear models to data as a direct matrix calculation,​ and incrementally using stochastic gradient descent (SGD)  | [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs445/​notebooks/​03 Linear Regression.ipynb|03 Linear Regression]]\\ [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs445/​notebooks/​04 Linear Regression Using Stochastic Gradient Descent (SGD).ipynb|04 Linear Regression Using Stochastic Gradient Descent (SGD)]]  ​
-| Week 3:\\ Jan 29 - Feb 2    | +| Week 3:\\ Jan 29 - Feb 2    ​| Ridge regression. Data partitioning. ​ Probabilistic Linear Regression. Regression with fixed nonlinearities. ​  | [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs445/​notebooks/​05 Linear Ridge Regression and Data Partitioning.ipynb|05 Linear Ridge Regression and Data Partitioning]]\\ [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs445/​notebooks/​06 Probabilistic Linear Regression.ipynb|06 Probabilistic Linear Regression]]\\ [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs445/​notebooks/​07 Linear Regression with Fixed Nonlinear Features.ipynb|07 Linear Regression with Fixed Nonlinear Features]] ​  ​|[[http://​www.deeplearningbook.org/​|Deep Learning]], Section 7.3\\  [[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.  | [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs445/​notebooks/​A1 Linear Regression.ipynb|A1 Linear Regression]] due Wednesday, January 31, 10:00 PM.  Here are some [[http://​www.cs.colostate.edu/​~anderson/​cs445/​goodSolutions|good solutions.]]  ​|
  
 ===== February ===== ===== February =====
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 |< 100% 10% 20% 30% 20% 20%  >| |< 100% 10% 20% 30% 20% 20%  >|
 ^  Week      ^  Topic      ^  Material ​ ^  Reading ​         ^  Assignments ​ ^ ^  Week      ^  Topic      ^  Material ​ ^  Reading ​         ^  Assignments ​ ^
-| Week 4:\\ Feb 5 - Feb 9   |  +| Week 4:\\ Feb 5 - Feb 9   | Introduction to nonlinear regression with neural networks. ​ | [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs445/​notebooks/​08 Stochastic Gradient Descent with Parameterized Activation Function.ipynb|08 Stochastic Gradient Descent with Parameterized Activation Function]]\\ [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs445/​notebooks/​09 Scaled Conjugate Gradient for Training Neural Networks.ipynb|09 Scaled Conjugate Gradient for Training Neural Networks]] ​ | [[http://​www.deeplearningbook.org/​|Deep Learning]], Chapter 6 (skip 6.2)  ​
-| Week 5:\\ Feb 12 - Feb 16  |  +| Week 5:\\ Feb 12 - Feb 16  | <color red>​Lectures on Feb 12th and 14th are canceled.</​color> ​ Friday, more neural networks ​ | [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs445/​notebooks/​10 More Nonlinear Regression with Neural Networks.ipynb|10 More Nonlinear Regression with Neural Networks]] ​ | 
-| Week 6:\\ Feb 19 - Feb 23  |  +| Week 6:\\ Feb 19 - Feb 23  | Autoencoders. Recurrent neural networks. ​ | | | [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs445/​notebooks/​A2 Neural Network Regression.ipynb|A2 Neural Network Regression]] due Tuesday, February 20, 10:00 PM  | 
-| Week 7:\\ Feb 26 - Mar 2  | +| Week 7:\\ Feb 26 - Mar 2  | Classification. LDA and QDA. K-Nearest Neighbors. ​ |
  
 ===== March ===== ===== March =====
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 |< 100% 10% 20% 30% 20% 20%  >| |< 100% 10% 20% 30% 20% 20%  >|
 ^  Week      ^  Topic      ^  Material ​ ^  Reading ​         ^  Assignments ​ ^ ^  Week      ^  Topic      ^  Material ​ ^  Reading ​         ^  Assignments ​ ^
-| Week 8:\\ Mar 5 - Mar 9   ​| ​+| Week 8:\\ Mar 5 - Mar 9   | Classification with Neural Networks  ​|
 |  Mar 12 - Mar 16  |  Spring Break  | |  Mar 12 - Mar 16  |  Spring Break  |
-| Week 9:\\ Mar 19 - Mar 23 |  +| Week 9:\\ Mar 19 - Mar 23 | Analysis of Trained Networks. Bottleneck Networks. Classifying Hand-Drawn Digits.  ​
-| Week 10:\\ Mar 26 - Mar 30  | +| Week 10:\\ Mar 26 - Mar 30  | Convolutional Neural Networks ​ |
  
 ===== April ===== ===== April =====
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 |< 100% 10% 20% 30% 20% 20%  >| |< 100% 10% 20% 30% 20% 20%  >|
 ^  Week      ^  Topic      ^  Material ​ ^  Reading ​         ^  Assignments ​ ^ ^  Week      ^  Topic      ^  Material ​ ^  Reading ​         ^  Assignments ​ ^
-| Week 11:\\ Apr 2 - Apr 6   |  +| Week 11:\\ Apr 2 - Apr 6   | Reinforcement Learning. Games using Tabular Q functions.  ​
-| Week 12:\\ Apr 9 - Apr 13  |  +| Week 12:\\ Apr 9 - Apr 13  | Reinforcement Learning using Neural Networks as Q functions. ​ | 
-| Week 13:\\ Apr 16 - Apr 20  |  +| Week 13:\\ Apr 16 - Apr 20  ​| Unsupervised Learning. Dimensionality Reduction. ​ Clustering. ​ |
-| Week 14:\\ Apr 23 - Apr 27  | +| Week 14:\\ Apr 23 - Apr 27  | Support Vector Machines. ​ |
  
 ===== May ===== ===== May =====
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 |< 100% 10% 20% 30% 20% 20%  >| |< 100% 10% 20% 30% 20% 20%  >|
 ^  Week      ^  Topic      ^  Material ​ ^  Reading ​         ^  Assignments ​ ^ ^  Week      ^  Topic      ^  Material ​ ^  Reading ​         ^  Assignments ​ ^
-| Week 15:\\ Apr 30 - May 4  | +| Week 15:\\ Apr 30 - May 4  | Ensembles. ​ Other topics. ​ |
 | May 7 - May 10  |  Final Exams  |  | May 7 - May 10  |  Final Exams  | 
  
  
- 
- 
- 
-/* 
-===== 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\\ <color red>No class March 22nd.</​color> ​ | 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. ​  | 
- 
-*/ 
  
start.1516485909.txt.gz · Last modified: 2018/01/20 15:05 by anderson