User Tools

Site Tools


start

Differences

This shows you the differences between two versions of the page.

Link to this comparison view

Both sides previous revision Previous revision
Next revision
Previous revision
Next revision Both sides next revision
start [2017/04/21 08:50]
anderson [April]
start [2019/01/03 15:25]
127.0.0.1 external edit
Line 1: Line 1:
 ====== 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 =====
Line 18: Line 9:
 |< 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/​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 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://​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)]]  |
  
 ===== February ===== ===== February =====
Line 26: Line 16:
 |< 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 Monday, January 30th at 10:00 PM.   |    +| Week 3:\\ Feb 4 - Feb 8    ​| 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 | | 
-| 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 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://​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 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 MondayFebruary 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/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  ​|  |
-| 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 =====
Line 36: Line 25:
 |< 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 =====
Line 44: Line 34:
 |< 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 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 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 Barto, 2nd 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 - 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 evening. You are welcome to start with a copy of the linked Google Doc.   | 
-| 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 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 =====
Line 53: Line 44:
 |< 100% 10% 20% 30% 20% 20%  >| |< 100% 10% 20% 30% 20% 20%  >|
 ^  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]] ​ | 
-| Finals Week:\\ May 8 May 11  |   |  |  |  Final project ​due Tuesday, May 9, 10:00 PM.  ​Details on report requirements will be posted here soon  ​|+| May 13 - May 16  |  Final Exams  |  |  |  ​|
  
  
  
start.txt · Last modified: 2019/02/16 16:40 by anderson