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start [2018/01/16 12:39]
anderson [January]
start [2019/06/03 13:46] (current)
anderson [May]
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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 =====
  
-/* +Thanks, everyone, for a fun semester. ​ I very much enjoyed reading your final reports. ​ Follow ​[[http://www.cs.colostate.edu/​~anderson/cs445/projects|this link]] to read the reports
-Lecture videos are available at this [[https://echo.colostate.edu/​ess/portal/section/​a5759ae3-82dc-43df-b515-dd944a6c4976|CS480 video recordings site]]+
-*/+
  
 ===== January ===== ===== January =====
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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]]  | Deep Learning, Chapters 1-5  | +| Week 1:\\  Jan 22 - Jan 25    | 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/​01 High-D Spaces.ipynb|01 High-D Spaces]]\\ [[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.scipy-lectures.org/​|Scipy Lectures]], Section 1\\ [[http://​www.deeplearningbook.org/​|Deep Learning]], Chapters 1 - 5.1.4  | 
-| Week 2:\\ Jan 22 Jan 26    ​| ​ +| Week 2:\\ Jan 28 Feb 1    ​| ​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://​www.deeplearningbook.org/​contents/​ml.html|Deep Learning, Section 5.1.4 and 5.9]]  ​|
-Week 3:\\ Jan 29 - Feb 2    ​+
  
 ===== 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 - Feb   |  +| Week 3:\\ Feb - Feb 8    | Stochastic gradient descent (SGD). Ridge regression. Data partitioning. ​ Probabilistic Linear Regression. Regression with fixed nonlinearities. ​  ​| [[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)]]\\ [[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/​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.  |    ​
-| Week 5:\\ Feb 12 - Feb 16  |  +| Week 4:\\ Feb 11 - Feb 15   | 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://​www.deeplearningbook.org/​|Deep Learning]], Chapter 6 (skip 6.2)  |  [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs445/​notebooks/​A1 Stochastic Gradient Descent for Simple Models.ipynb|A1 Stochastic Gradient Descent for Simple Models]] due Tuesday, February 12, 10:00 PM.\\ [[http://​www.cs.colostate.edu/​~anderson/​cs445/​notebooks/​good-ones/​|Examples of good solutions]] ​  
-| Week 6:\\ Feb 19 - Feb 23  |  +| Week 5:\\ Feb 18 - Feb 22  ​| ​More neural networks ​ | [[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://​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 7:\\ Feb 26 - Mar  ​| ​+| Week 6:\\ Feb 25 - Mar 1  | Autoencoders. Activation functions. ​ | [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs445/​notebooks/​11 Autoencoder Neural Networks.ipynb|11 Autoencoder Neural Networks]] ​ | [[https://​arxiv.org/​pdf/​1710.05941.pdf|Searching for Activation Functions]],​ by Ramachandran,​ Zoph, and Le\\ [[http://​aclweb.org/​anthology/​D18-1472|Is it Time to Swish? Comparing Deep Learning Activation Functions Across NLP tasks]], by Eger, Youssef, and Gurevych ​  ​| ​ [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs445/​notebooks/​A2 Adam vs SGD.ipynb|A2 Adam vs SGD]] due Tuesday February 26, 10:00 PM.  |
  
 ===== 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 - Mar   |  +| Week 7:\\ Mar 4 - Mar 8  | Classification. LDA and QDA. K-Nearest Neighbors. ​ | [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs445/​notebooks/​12 Introduction to Classification.ipynb|12 Introduction to Classification]] \\ [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs445/​notebooks/​13 Gaussian Distributions.ipynb|13 Gaussian Distributions]] ​  | [[https://​towardsdatascience.com/​jupyter-lab-evolution-of-the-jupyter-notebook-5297cacde6b|Jupyter Lab: Evolution of the Jupyter Notebook]] by Parul Pandey ​ | | 
-|  Mar 12 - Mar 16  ​| ​ Spring Break  | +| Week 8:\\ Mar 11 - Mar 15   ​| Classification with Neural Networks ​ | [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs445/​notebooks/​14 Classification with Linear Logistic Regression.ipynb|14 Classification with Linear Logistic Regression]]\\ [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs445/​notebooks/​15 Classification with Nonlinear Logistic Regression Using Neural Networks.ipynb|15 Classification with Nonlinear Logistic Regression Using Neural Networks]] ​ |  | [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs445/​notebooks/​A3 Neural Network Regression and Activation Functions.ipynb|A3 Neural Network Regression and Activation Functions]] due Friday March 15, 10:00 PM.  ​
-| Week 9:\\ Mar 19 - Mar 23  +|  Mar 18 - Mar 22  ​| ​ Spring Break  | 
-Week 10:\\ Mar 26 - Mar 30  ​| ​+| Week 9:\\ Mar 25 - Mar 29 Pytorch.  ​[[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs445/​notebooks/​16 Introduction to Pytorch.ipynb|16 Introduction to Pytorch]] ​ | | |
  
 ===== 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 - Apr   |  +| Week 10:\\ Apr 1 - Apr 5  | Pytorch. Convolutional Neural Networks ​ | [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs445/​notebooks/​17 Pytorch autograd, nn.Module.ipynb|17 Pytorch autograd, nn.Module]]\\ [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs445/​notebooks/​18 Convolutional Neural Networks.ipynb|18 Convolutional Neural Networks]]\\ [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs445/​notebooks/​19 Convolutional Neural Networks in Pytorch.ipynb|19 Convolutional Neural Networks in Pytorch]] ​ | [[https://​towardsdatascience.com/​getting-started-with-pytorch-part-1-understanding-how-automatic-differentiation-works-5008282073ec|Pytorch Automatic Differentiation]]\\ [[https://​www.youtube.com/​watch?​v=MswxJw-8PvE|PyTorch Autograd Explained - In-depth Tutorial]], by Elliott Waite   ​| ​ | 
-| Week 12:\\ Apr - Apr 13  |  +| Week 11:\\ Apr - Apr 12   | Reinforcement Learning. Games using Tabular Q functions. ​ | [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs445/​notebooks/​22 Introduction to Reinforcement Learning.ipynb|22 Introduction to Reinforcement Learning]]\\ [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs445/​notebooks/​23 Reinforcement Learning with Neural Network as Q Function.ipynb|23 Reinforcement Learning with Neural Network as Q Function]] ​ | [[http://​incompleteideas.net/​book/​the-book.html|Reinforcement Learning: An Introduction]],​ by Sutton and Barto, 2nd ed.  | [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs445/​notebooks/​Project Proposal.ipynb|Project proposal]] due at 10 pm Friday evening. ​  | 
-| Week 13:\\ Apr 16 - Apr 20  |  +| Week 12:\\ Apr 15 - Apr 19  | Reinforcement Learning using Neural Networks as Q functions. ​ | [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs445/​notebooks/​24 Reinforcement Learning to Control a Marble.ipynb|24 Reinforcement Learning to Control a Marble]]\\ [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs445/​notebooks/​25 Reinforcement Learning for Two Player Games.ipynb|25 Reinforcement Learning for Two Player Games]] ​  ​| ​  | [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs445/​notebooks/​A4 Classifying Hand-Drawn Digits.ipynb |A4 Classifying Hand-Drawn Digits]] due Wednesday, April 17  | 
-| Week 14:\\ Apr 23 Apr 27  ​| ​+| Week 13:\\ Apr 22 - Apr 26  | Unsupervised Learning. Dimensionality Reduction. ​ Clustering. ​ | [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs445/​notebooks/​26 Genetic Algorithm Search.ipynb|26 Genetic Algorithm Search]]\\ [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs445/​notebooks/​27 Linear Dimensionality Reduction.ipynb|27 Linear Dimensionality Reduction]]\\ [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs445/​notebooks/​28 K-Means Clustering.ipynb|28 K-Means Clustering]] ​ | 
 +| Week 14:\\ Apr 29 May 3  ​| ​Hierarchical clustering. K Nearest Neighbors Classification. Support Vector Machines. ​ | [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs445/​notebooks/​29 Hierarchical Clustering.ipynb|29 Hierarchical Clustering]]\\ [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs445/​notebooks/​30 Nonparametric Classification with K Nearest Neighbors.ipynb|30 Nonparametric Classification with K Nearest Neighbors]]\\ [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs445/​notebooks/​31 Support Vector Machines.ipynb|31 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  |  +| Week 15:\\ May 6 - May 10  | Ensembles. ​ Other topics. ​ | [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs445/​notebooks/​32 Ensembles of Convolutional Neural Networks.ipynb|32 Ensembles of Convolutional Neural Networks]]\\ [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs445/​notebooks/​33 Machine Learning for Brain-Computer Interfaces.ipynb|33 Machine Learning for Brain-Computer Interfaces]]\\ ​ [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs445/​notebooks/​34 Modeling Global Climate Change.ipynb|34 Modeling Global Climate Change]] ​ |  |  | 
-| May - May 10  ​| ​ Final Exams  | +| May 13 - May 16  ​| ​ Final Exams  ​| ​ |  | Final Project Report due Tuesday, May 14, 10:00 PM. Here are is a [[http://​www.cs.colostate.edu/​~anderson/​cs445/​projects|links to most of the project reports]] ​ |
  
  
- 
- 
- 
-/* 
-===== 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.]] | 
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-===== May ===== 
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-|< 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. ​  | 
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start.1516131590.txt.gz · Last modified: 2018/01/16 12:39 by anderson