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start [2017/04/14 08:47]
anderson [April]
start [2019/02/16 16:40] (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 =====
  
-**March 20:** A4grader.tar linked to on the A4 web page has been updated. It longer checks for QDA-related functions. +This is a tentative schedule ​Changes ​will be made as the semester progresses.
- +
-**March 18:** There will be no lecture class on Wednesday, March 22nd.  Chuck'​s office hours on March 22nd are cancelled. +
- +
-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 17 - Jan 20    | 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 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 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]]      |    +| 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]]  |
  
 ===== 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 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 MondayJanuary 30th at 10:00 PM  ​  ​ +| Week 3:\\ Feb 4 - Feb 8    ​Stochastic gradient descent (SGD). Ridge regression. Data partitioning.  ​Probabilistic Linear RegressionRegression 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 4:\\ Feb - 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 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.  ​
-| 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 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 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 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://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs445/notebooks/A2 Adam vs SGD.ipynb|A2 Adam vs SGD]] due Monday February 25, 10:00 PM.  |
-| 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 WednesdayMarch 1st at 10:00 PM.\\ Here are [[A3-good-ones|examples of good A3 reports.]] ​ ​| ​   +
  
 ===== 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 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 7:\\ Mar - 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]]   ​|  ​
-| Week 9:\\ Mar 20, Mar 24\\ <color red>No class March 22nd.</​color>  ​| Classification. Analysis of Trained ​Networks. ​Bottleneck NetworksHand-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 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/16 Introduction to Pytorch.ipynb|16 Introduction to Pytorch]]  ​
-| 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.htmlReinforcement Learning: An Introduction]], by Richard Sutton and Andrew Barto. 2nd edition draft. On-line and free.  ​| ​ + Mar 18 - Mar 22  ​| ​ ​Spring Break  | 
 +| Week 9:\\ Mar 25 - Mar 29 Analysis of Trained ​Networks. ​Bottleneck Networks. Classifying Hand-Drawn Digits.  | [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs445/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/​cs445/notebooks/18 Dealing with Time Series by Time-Embedding.ipynb|18 Dealing with Time Series by Time-Embedding]]\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/​cs445/​notebooks/​19 Recurrent Neural Networks.ipynb|19 Recurrent 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 - 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 ​Barto2nd edition draftOn-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.\\ [[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 10:\\ Apr - Apr 5  ​Convolutional Neural Networks ​ | [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs445/​notebooks/​20 Classifying Hand-drawn Digits.ipynb|20 Classifying Hand-drawn Digits]]\\ ​[[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs445/​notebooks/​21 ​Convolutional Neural Networks.ipynb|21 Convolutional Neural Networks]]\\ [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs445/​notebooks/​22 Introduction to Reinforcement Learning.ipynb|22 Introduction to Reinforcement Learning]] ​ | [[http://​incompleteideas.net/​book/​the-book.html|Reinforcement Learning: An Introduction]],​ by Sutton and Barto2nd ed  |  | 
-| 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 11:\\ Apr 8 Apr 12   | Reinforcement Learning. Games using Tabular Q functions.  | [[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]] ​ |  | [[https://​drive.google.com/​open?​id=1KHAxeIwL3ait2ZUbILdbJjCLW47JwxKpdjsAr5kkkZk|Project proposal]] due at 10 pm Friday eveningYou are welcome to start with a copy of the linked Google Doc.   | 
-| Week 13:\\ Apr 17 Apr 21  ​| ​ |  ​| ​ |  | +| 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]]   | 
-| Week 14:\\ Apr 24 - Apr 28  |  |  |  | [[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. |+| Week 13:\\ Apr 22 - Apr 26  | Unsupervised Learning. Dimensionality Reduction. ​ Clustering. ​ | [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs445/notebooks/26 Linear Dimensionality Reduction.ipynb|26 Linear Dimensionality Reduction]]\\ [[http://​nbviewer.ipython.org/​url/www.cs.colostate.edu/~anderson/cs445/notebooks/​27 Examples ​of Linear Dimensionality Reduction.ipynb|27 Examples ​of 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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 ^  Week      ^  Topic      ^  Material ​ ^  Reading ​         ^  Assignments ​ ^ ^  Week      ^  Topic      ^  Material ​ ^  Reading ​         ^  Assignments ​ ^
-| Week 15:\\ May - May 5     ​|  |  |  |+| Week 15:\\ May - 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]] ​ |  | Final Project Report due Wednesday, May 8, 10:00 PM. Here is a [[http://​nbviewer.ipython.org/​url/​www.cs.colostate.edu/​~anderson/​cs445/​notebooks/​Project Report Example.ipynb|Project Report Example]] ​ | 
 +| May 13 - May 16  |  Final Exams  ​|  |  |  |
  
  
  
start.1492181233.txt.gz · Last modified: 2017/04/14 08:47 by anderson