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schedule [2016/12/14 07:52]
anderson
schedule [2024/01/08 18:40] (current)
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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 =====
  
-/*+Links to live MS Teams events: 
 +  * Lectures: [[https://teams.microsoft.com/l/meetup-join/19%3a323d2d59a8f64282b836e440b8cb32d9%40thread.tacv2/1598126257845?context=%7b%22Tid%22%3a%22afb58802-ff7a-4bb1-ab21-367ff2ecfc8b%22%2c%22Oid%22%3a%22bcd6d782-40c2-430e-8091-fd9ebd260de7%22%7d|Tuesdays/Thursdays, 2:00 - 3:15 PM]] 
 +  Office hours: Apoorv [[https://teams.microsoft.com/l/meetup-join/19%3a323d2d59a8f64282b836e440b8cb32d9%40thread.tacv2/1598300599034?context=%7b%22Tid%22%3a%22afb58802-ff7a-4bb1-ab21-367ff2ecfc8b%22%2c%22Oid%22%3a%22bcd6d782-40c2-430e-8091-fd9ebd260de7%22%7d|Mondays, 2:00 - 4:00 PM]] 
 +  * Office hours: Chaitanya [[https://teams.microsoft.com/l/meetup-join/19%3a323d2d59a8f64282b836e440b8cb32d9%40thread.tacv2/1598301087268?|Fridays, 2:00 - 4:00 PM]] 
 +  * Office hours: Chuck [[https://teams.microsoft.com/l/meetup-join/19%3a323d2d59a8f64282b836e440b8cb32d9%40thread.tacv2/1598288070646?|Wednesdays, 9:00 - 10:00 AM]] 
 + 
  
-**May 9:** At the bottom of this page is a link to a summary of the content expected in your project reports.+Recordings of lecture and office hour videos are available from the Home page of our  
 +[[https://colostate.instructure.com/courses/109411|Canvas site]].
  
-**April 29:** My latest neural network code is available at [[http://www.cs.colostate.edu/~anderson/cs480/notebooks/nn7.tar|nn7.tar]].+To use jupyter notebooks on our CS department machines, you must add this line to your .bashrc file:
  
-*/ +  export PATH=/usr/local/anaconda/bin:$PATH
-===== January =====+
  
-|< 100% 20% 20% 30% 10% 20%  >| +This is a tentative schedule of CS440 topics for Fall2020.  This will be updated during the summer and as the fall semester continues.
-^  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]],  | Text: Sections 1.1-1.5. 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]],\\ [[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]]    | Sections 4.1-4.2, 4.6-4.9, 5.8-5.9      [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/A1 Linear Regression.ipynb|A1 Linear Regression]] due Friday, January 29th at 10:00 PM. Download and unzip [[http://www.cs.colostate.edu/~anderson/cs480/notebooks/A1 Grader.zip|A1 Grader.zip]]\\ Here are five examples of good solutions: [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/A1good/A1a.ipynb|A1a]], [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/A1good/A1b.ipynb|A1b]], [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/A1good/A1c.ipynb|A1c]], [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/A1good/A1d.ipynb|A1d]], [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/A1good/A1e.ipynb|A1e]]   |  +
  
-/* 
  
-===== February =====+===== August =====
  
-|< 100% 20% 20% 3010% 20%  >|+|< 100% 18% 20% 2220% 20%  >|
 ^  Week      ^  Topic      ^  Material  ^  Reading          ^  Assignments  ^ ^  Week      ^  Topic      ^  Material  ^  Reading          ^  Assignments  ^
-| Week 3:\\ Feb 1 Feb 5      Ridge regression. Data partitioning. On-line, incremental regression. Regression with fixed nonlinearities.  | [[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/09 Linear Regression with Fixed Nonlinear Features.ipynb|09 Linear Regression with Fixed Nonlinear Features]]    | | +| Week 1:\\  Aug 24 Aug 28    What is AI?  Promises and fears.\\ Python review.\\ Problem-Solving Agents | [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/01 Introduction to AI.ipynb|01 Introduction to AI]]\\ [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/02 Introduction to Python.ipynb|02 Introduction to Python]]   Chapters 1, 23.1 of Russell and Norvig.\\ Section 1 of [[http://www.scipy-lectures.org|Scipy Lecture Notes]]  \\ [[http://science.sciencemag.org/content/357/6346/7.full|AI, People, and Society]], by Eric Horvitz.\\ [[https://aeon.co/essays/can-we-design-machines-to-make-ethical-decisions|Automated Ethics]], by Tom Chatfield.\\ [[http://www.nytimes.com/2016/12/14/magazine/the-great-ai-awakening.html?_r=0|The Great A.IAwakening]], by Gideon Lewis-Krause\\ <!-- [[https://www.commondreams.org/news/2017/07/19/fundamental-existential-threat-lawmakers-warned-risks-killer-robots|"Fundamental Existential Threat": Lawmakers Warned of the Risks of Killer Robots]], by Julia Conley\\ -->    
-| Week 4:\\ Feb 8 Feb 12     | Nonlinear regression with 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]]  11.1-11.511.7.111.7.4, 11.8.1-11.8.2  |  +
-| Week 5:\\ Feb 15 - Feb 19    | Autoencoders. Recurrent neural networks.     [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/12 Autoencoder Neural Networks.ipynb|12 Autoencoder Neural Networks]]\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/13 Recurrent Neural Networks.ipynb|13 Recurrent Neural Networks]]   | 11.911.12, 11.14   |  [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/A2 Linear Regression with Fixed Nonlinear Features.ipynb|A2 Linear Regression with Fixed Nonlinear Features]] due MondayFeb 15 at 10:00 PM.\\ Here are three examples of good solutions: [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/A2good/A2a.ipynb|A2a]], [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/A2good/A2b.ipynb|A2b]], [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/A2good/A2c.ipynb|A2c]]   | +
-| Week 6:\\ Feb 22 Feb 26    Classification, generative models.   | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/14 Introduction to Classification.ipynb|14 Introduction to Classification]]   | 4.3-4.5, 5.5-5.7  |+
  
-===== March =====+===== September =====
  
-|< 100% 20% 20% 3010% 20%  >| +|< 100% 18% 20% 2220% 20%  >| 
-^  Week      ^  Topic      ^  Material  ^  Reading          ^  Assignments +^  Week      ^  Topic      ^  Material  ^  Reading          ^  Assignments  ^nderson/cs545/doku.php?id=schedule#september 
-| Week 7:\\ Feb 29 Mar 5     Classification, Introduction to Support Vector Machines  | Monday: GTA Jake Lee will discuss questions on Assignment 3.  Wednesday: Guest lecture by Dr. Asa Ben-Hur.\\ [[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://www.cs.colostate.edu/~anderson/cs480/notebooks/svms-asa.pdf|SVM Slides]]  | 10.1-10.4, 10.5-10.10    | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/A3 Neural Network Regression.ipynb|A3 Neural Network Regression]] due Monday, Feb 29 at 10:00 PM.\\ Here are examples of good solutions: [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/A3good/A3a.ipynb|A3a]], [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/A3good/A3b.ipynb|A3b]], [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/A3good/A3c.ipynb|A3c]],[[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/A3good/A3d.ipynb|A3d]][[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/A3good/A3e.ipynb|A3e]][[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/A3good/A3f.ipynb|A3f]],[[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/A3good/A3g.ipynb|A3g]], [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/A3good/A3h.ipynb|A3h]]  | +| Week 2:\\ Aug 31 Sept 4    Help with A1.\\ Problem-solving search and how to measure performance.\\ Iterative deepening and other uninformed search methods.   [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/03 Problem-Solving Agents.ipynb|03 Problem-Solving Agents]]\\ [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/04 Measuring Search Performance.ipynb|04 Measuring Search Performance]]\\ [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/05 Iterative Deepening and Other Uninformed Search Methods.ipynb|05 Iterative Deepening and Other Uninformed Search Methods]]   | Sections 3.1 - 3.4 of Russell and Norvig  |   |  
-| Week 8:\\ Mar 7 Mar 11     Classification with neural networks.     | [[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]]  | 11.7.2     | +| Week 3:\\ Sept 7 - Sept 11  | Informed searchA* searchPython classes, sorting, numpy arrays | [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/06 Python Implementation of Iterative Deepening.ipynb|06 Python Implementation of Iterative Deepening]]\\ [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/07 Informed Search.ipynb|07 Informed Search]]\\ [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/08 Python Classes.ipynb|08 Python Classes]]   | Rest of Chapter 3  | [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/A1.1 Uninformed Search.ipynb|A1.1 Uninformed Search]] due TuesdaySept. 8, 10:00 PM Submit your notebook in Canvas.\\ Here are [[http://www.cs.colostate.edu/~anderson/cs440/notebooks/goodones|good solutions from your classmates]]  | 
-|  Mar 14 - Mar 18    | Spring Break!    |       | +| Week 4:\\ Sept 14 Sept 18   A* optimality, admissible heuristics  | [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/09 Heuristic Functions.ipynb|09 Heuristic Functions]]\\ [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/10 Local Search.ipynb|10 Local Search]]   | Chapter 4  [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/A2.1 Iterative-Deepening Search.ipynb|A2.1 Iterative-Deepening Search]] due TuesdaySept15, 10:00 PM.\\ Here are [[http://www.cs.colostate.edu/~anderson/cs440/notebooks/goodones|good solutions from your classmates]]  | 
-| Week 9:\\ Mar 21 - Mar 25    | Bottleneck, and deep networks.    | [[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]]  | 11.8.311.1111.13     |  +| Week 5:\\ Sept 21 - Sept 25   | Effective branching factor.\\ Local search and optimizationAdversarial searchMinimaxAlpha-beta pruningStochastic games | [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/11 Adversarial Search.ipynb|11 Adversarial Search]]   Chapter 5  | 
-| Week 10:\\ Mar 28 - Apr 1    | Convolutional neural nets. Clustering. [[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 Clustering.ipynb|20 Clustering]] \\  [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/21 Mixtures of Gaussians.ipynb|21 Mixtures of Gaussians]]   7.1-7.10  [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/A4 Classification with LDAQDA, and Logistic Regression.ipynb|A4 Classification with LDA, QDA, and Logistic Regression]] due Tuesday, March 29 at 10:00 PMHere are examples of good solutions: [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/A4good/a4a.ipynb|a4a]], [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/A4good/a4b.ipynb|a4b]][[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/A4good/a4c.ipynb|a4c]],[[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/A4good/a4d.ipynb|a4d]][[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/A4good/a4e.ipynb|a4e]][[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/A4good/a4f.ipynb|a4f]],[[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/A4good/a4g.ipynb|a4g]], [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/A4good/a4h.ipynb|a4h]]  |+| Week 6:\\ Sept 28 - Oct 2   | Negamax, with pruningIntroduction to Reinforcement Learning | [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/12 Negamax.ipynb|12 Negamax]]\\ [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/13 Modern Game Playing.ipynb|13 Modern Game Playing]]\\  [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/14 Introduction to Reinforcement Learning.ipynb|14 Introduction to Reinforcement Learning]]      | Chapter 21\\ [[http://incompleteideas.net/book/bookdraft2017nov5.pdf|Reinforcement Learning: An Introduction]]   |   [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/A3 A*, IDS, and Effective Branching Factor.ipynb|A3 A*, IDS, and Effective Branching Factor]] due WednesdaySept. 30, 10:00 PM.\\ Here are [[http://www.cs.colostate.edu/~anderson/cs440/notebooks/goodones|good solutions from your classmates]]  |
  
-===== April =====+===== October =====
  
-|< 100% 20% 20% 3010% 20%  >|+|< 100% 18% 20% 2220% 20%  >|
 ^  Week      ^  Topic      ^  Material  ^  Reading          ^  Assignments  ^ ^  Week      ^  Topic      ^  Material  ^  Reading          ^  Assignments  ^
-| Week 11:\\ Apr 4 Apr      | Reinforcement Learning  [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/22 Introduction to Reinforcement Learning.ipynb|22 Introduction to Reinforcement Learning]]\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/23 Reinforcement Learning for Two Player Games.ipynb|23 Reinforcement Learning for Two Player Games]]\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/24 Reinforcement Learning with Neural Network as Q Function.ipynb|24 Reinforcement Learning with Neural Network as Q Function]]  | 18.1-18.9  | +| Week 7:\\ Oct 5 Oct 9\\ <color red>Oct Lecture will not meet, but recording will be available.</color>  | Reinforcement Learning for Two-Player Games | [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/15 Reinforcement Learning for Two-Player Games.ipynb|15 Reinforcement Learning for Two-Player Games]]   | Chapter 21\\ [[http://incompleteideas.net/book/bookdraft2017nov5.pdf|Reinforcement Learning: An Introduction]]  |  | 
-| Week 12:\\ Apr 11 Apr 15    Dimensionality reduction.  |  [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/25 Tic-Tac-Toe with Neural Network Q Function.ipynb|25 Tic-Tac-Toe with Neural Network Q Function]]\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/26 Linear Dimensionality Reduction.ipynb|26 Linear Dimensionality Reduction]]\\  [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/27 Nonlinear Dimensionality Reduction with Digits Example.ipynb|27 Nonlinear Dimensionality Reduction with Digits Example]]  | 6.1-6.8, 6.10-6.13  | +| Week 8:\\ Oct 12 Oct 16  Constraint satisfaction.\\ Min-conflicts.  | [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/16 Constraint Satisfaction Problems.ipynb|16 Constraint Satisfaction Problems]]\\ [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/17 Min-Conflicts.ipynb|17 Min-Conflicts]]  <!-- \\ [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/25 Min-Conflicts in Python with Examples.ipynb|25 Min-Conflicts in Python with Examples]] -->   | Chapter 6  | 
-| Week 13:\\ Apr 18 Apr 22    | Nonparametric methods  | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/28 Nonparametric Classification with K Nearest Neighbors.ipynb|28 Nonparametric Classification with K Nearest Neighbors]][[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/29 Support Vector Machines.ipynb|29 Support Vector Machines]]  | 8.1-8.10  | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/A5 Reinforcement Learning Solution to Visual Tic-Tac-Toe.ipynb|A5 Reinforcement Learning Solution to Visual Tic-Tac-Toe]] due Wednesday, April 20 at 10:00 PM.\\ Here are examples of good solutions: [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/A5good/a.ipynb|a5a]][[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/A5good/b.ipynb|a5b]][[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/A5good/c.ipynb|a5c]],[[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/A5good/d.ipynb|a5d]], [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/A5good/e.ipynb|a5e]], [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/A5good/f.ipynb|a5f]],[[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/A5good/g.ipynb|a5g]]\\  Check in your [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/Project Proposal.ipynb|Project Proposal]] by Friday, April 22nd, at 10:00 PM  | +| Week 9:\\ Oct 19 Oct 23  | Natural language processing.   | [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/18 Introduction to Natural Language Processing.ipynb|18 Introduction to Natural Language Processing]]\\ [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/19 More NLP.ipynb|19 More NLP]] <!-- \\ [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/27 Word Embeddings.ipynb|27 Word Embeddings]] --> [[https://towardsdatascience.com/word-embedding-with-word2vec-and-fasttext-a209c1d3e12c|Word2Vec and FastText Word Embedding with Gensim]]  |  [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/A4 Reinforcement Learning Solution To Towers of Hanoi.ipynb|A4 Reinforcement Learning Solution To Towers of Hanoi]] due TuesdayOct. 20, 10:00 PM. Here are [[http://www.cs.colostate.edu/~anderson/cs440/notebooks/goodones|good solutions from your classmates]]  | 
-| Week 14:\\ Apr 25 - Apr 29    | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/31 Machine Learning for Brain-Computer Interfaces.ipynb|31 Machine Learning for Brain-Computer Interfaces]], [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/32 Comparison of Algorithms for BCI.ipynb|32 Comparison of Algorithms for BCI]], [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/33 Convolutional Neural Networks for BCI.ipynb|33 Convolutional Neural Networks for BCI]]  |+| Week 10:\\ Oct 26 - Oct 30  | Introduction to Neural Networks  | [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/20 Introduction to Neural Networks.ipynb|20 Introduction to Neural Networks]]\\  [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/21 Pytorch Neural Networks.ipynb|21 Pytorch Neural Networks]]   <!-- \\ [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/17 More Introduction to Neural Networks.ipynb|17 More Introduction to Neural Networks]]  -->  Sections 18.6 and 18.7    | [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/A5.1 Min-Conflicts.ipynb|A5.1 Min-Conflicts]] due Friday Oct 3010:00 PMHere are [[http://www.cs.colostate.edu/~anderson/cs440/notebooks/goodones|good solutions from your classmates]]   |
  
  
 +===== November =====
 +
 +|< 100% 18% 20% 22% 20% 20%  >|
 +^  Week      ^  Topic      ^  Material  ^  Reading          ^  Assignments  ^
 +| Week 11:\\ Nov 2 - Nov 6  | More Neural Networks  | [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/22 Classification with Pytorch Neural Networks.ipynb|22 Classification with Pytorch Neural Networks]]  <!-- [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/17 More Introduction to Neural Networks.ipynb|17 More Introduction to Neural Networks]]\\ [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/22 Autoencoder Neural Networks.ipynb|22 Autoencoder Neural Networks]] -->  |    |     |
 +| Week 12:\\ Nov 9 - Nov 13  | Interpreting what a neural network has learned.   | [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/23 Interpreting What a Neural Network Has Learned.ipynb|23 Interpreting What a Neural Network Has Learned]]   <!-- [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/18 Introduction to Classification.ipynb|18 Introduction to Classification]] -->  |     | [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/A6 Neural Networks.ipynb|A6 Neural Networks]] due <color red>Sunday Nov 15, 10:00 PM.</color>\\ Here are [[http://www.cs.colostate.edu/~anderson/cs440/notebooks/goodones|good solutions from your classmates]]   |
 +| Week 13:\\ Nov 16 - Nov 20  | Natural language processing with neural nets.   | [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/24 NLP With Transformers.ipynb|24 NLP With Transformers]]  <!-- [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/19 Classification with Linear Logistic Regression.ipynb|19 Classification with Linear Logistic Regression]]\\ [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/20 Classification with Nonlinear Logistic Regression Using Neural Networks.ipynb|20 Classification with Nonlinear Logistic Regression Using Neural Networks]]\\ [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/21 Reinforcement Learning with a Neural Network as the Q Function.ipynb|21 Reinforcement Learning with a Neural Network as the Q Function]]  -->  |    |  |
 +|  Nov 23 - Nov 27  |  Fall Recess!  |
  
-===== May =====+===== December =====
  
-|< 100% 20% 20% 3010% 20%  >|+|< 100% 18% 20% 2220% 20%  >|
 ^  Week      ^  Topic      ^  Material  ^  Reading          ^  Assignments  ^ ^  Week      ^  Topic      ^  Material  ^  Reading          ^  Assignments  ^
-| Week 15:\\ May 2 May 6    Multiple models.\\ PLEASE ATTEND MAY 6th LECTURE TO FILL OUT THE ASCSU STUDENT COURSE SURVEYS! Distance-section students will be filling out the survey on-line  | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/34 Ensembles of Convolutional Neural Networks.ipynb|34 Ensembles of Convolutional Neural Networks]][[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/35 Ensembles of Convolutional Neural Networks for BCI.ipynb|35 Ensembles of Convolutional Neural Networks for BCI]]  | 17.1-17.12   |+| Week 14:\\ Nov 30 Dec 4  Clustering. Word embeddings.\\ Genetic algorithms | [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/25 Clustering of Word Embeddings.ipynb|25 Clustering of Word Embeddings]]\\ [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/26 Genetic Algorithm Search.ipynb|26 Genetic Algorithm Search]]   |  [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/A7.1 NLP with Transformers and the T5 Model.ipynb|A7.1 NLP with Transformers and the T5 Model]] due Sunday, Dec 6, 10:00 PM\\ Here are [[http://www.cs.colostate.edu/~anderson/cs440/notebooks/goodones|good solutions from your classmates]]   |  
 +| Week 15:\\ Dec 7 Dec 11  | Brain-Computer Interfaces. Pre-training for faster reinforcement learning.       |  
 +| Final Exam Week:\\ Dec 14 - Dec 18  | No exam.    |  | | [[https://nbviewer.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/A8 Report Template.ipynb|A8 Report Template]] due Tuesday, December 15th, 10:00 PM.   |
  
-| Week 16:\\ May 10    | Final Project Notebook Due.    | | | Check in final project notebook by Tuesday, May 10th, at 10:00 PM. [[Final Project Report|Here is a summary]] of what is expected in your reportsl  | 
  
-Selected Project Reports (in no particular order): 
  
-  * [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/Projects/dayalex_67206_4742841_Day FinalProject.ipynb|Tracking GLR-1 Receptors in Time-Lapse Imaging]] 
-  * [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/Projects/torresandy_23458_4746802_Torres AFinal.ipynb|Kaggle's Santander Data Analysis and Classification]] 
-  * [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/Projects/Calderon Jaramillo Report.ipynb|Reinforcement Learning in Pong]] 
-  * [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/Projects/mishraankit_38357_4746332_Mishra_Final_Project-2.ipynb|Evaluating factors affecting pricing in California Markets utilizing Machine Learning methods. ]] 
-  * [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/Projects/arkenbergadrion_483_4743731_Arkenberg Project.ipynb|Classifying Raptor Feathers Using Convolutional Neural Networks (CNNs)]] 
-  * [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/Projects/EasonFinalProject.ipynb|Exploring Reddit Karma Trends]] 
-  * [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/Projects/porecasey_52217_4747688_cpore_final_project-1.ipynb|A Comparison of Classifiers on Digitally Captured Handwritten Digits]] 
-  * [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/Projects/Yang Project.ipynb|Applying Neural Network Regrssion on BMP Database]] 
-  * [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/Projects/Connect-4 with Neural Network Q Function.ipynb|Connect-4 with Neural Network Q Function]] 
-  * [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/Projects/Douda Final Report.ipynb|Modeling Red and White Wine Quality From Multiple Factors]] 
-  * [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/Projects/Torain Project.ipynb|Trying to determine Customer Satisfaction based on numeric variables]] 
-  * [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/Projects/iyerrohit_34532_4748004_Final_Project.ipynb|Sentiment Analysis on Amazon Product Reviews]] 
-  * [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/Projects/Dale Final Project.ipynb|Estimating Income Based Off Socioeconomic Factors]] 
-  * [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/Projects/Edwards Final Project.ipynb|Clustering of Genomic Functional Elements]] 
-  * [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/Projects/Herman Final Project.ipynb|Predicting NBA Team Wins]] 
-  * [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/Projects/larisonjosh_52678_4747696_Larison Final Project.ipynb|Applying Logistic Regression to EEG P300 Waves]] 
-  * [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/Projects/ODell Project.ipynb|Prediction of Stock Market, in Near Future]] 
-  * [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/Projects/rustjonathan_20568_4744501_Rust Term Project-1.ipynb|Predicting the Next Pitch in Baseball]] 
-  * [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/Projects/vanettenjeff_60636_4747400_VanEtten project-1.ipynb|Is there a relationship between atmospheric CO2 levels and/or sunspots and hurricane activity in the Atlantic Ocean?]] 
-  * [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/Projects/Project Report Follmer.ipynb|Analyzing StarCraft Players and Game Details]] 
-  * [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/Projects/Kurth finalProject.ipynb|Neural Network Modeling of NFL data to predict Fantasy Football defense]] 
-  * [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/Projects/Strand Project.ipynb|Simplified New View Synthesis with Convolutional Neural Networks]] 
-  * [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/Projects/Lakin_FinalProject.ipynb|Neural Networks using Theano for the Data Mining Cup 2016]] 
-  * [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/Projects/benfrajmohtadi_98144_4747591_BenFraj Project.ipynb|Expedia Hotel Recommendations]] 
-  * [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/Projects/Kerry McKean Final Project Report.ipynb|Neural Network Regression for Musical Prediction]] 
-  * [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/Projects/FinalReportCarbonariRyan.ipynb|Comparison of Classification Techniques to Detect the Higgs Boson]] 
-  * [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/Projects/myergreg_54762_4744409_Final Myer.ipynb|Predicting Ionosphere Severity Using A Neural Network]] 
-  * [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/Projects/whitehillnick_39527_4746200_Whitehill Final Report.ipynb|Ultimate Tic Tac Toe]] 
-  * [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/Projects/Olson Project.ipynb|Predicting NFL QB Passing Yards]] 
-  * [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/Projects/Sharma Project.ipynb|Neural Network vs Linear Regression analysis on the Dow Jones Industrial Average]] 
-  * [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/Projects/Hamor Project.ipynb|Reinforcement Learning with Neural Network Q Function]] 
-  * [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/Projects/Szejna Final.ipynb|Multilabel classification of emotions in music using Neural Networks]] 
-  * [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/Projects/Diede Final Project Report.ipynb|Predicting the Geographic Origin of Ethnic Traditional Music with Supervised Machine Learning]] 
-  * [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/Projects/Haiming Final Project.ipynb|Tensorflow Versus Our Neural Network Implementation]] 
-  * [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/Projects/martstyler_36730_4747946_Marts Final Project.ipynb|Machine Learning and Sports Analytics in the NHL]] 
-  * [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/Projects/xuliyan_59738_4748165_Xu Project (1).ipynb|Simplified Go Game with Neural Network Q Function]] 
-  * [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/Projects/Tao final project.ipynb|The k-means optimizing algorithm]] 
-  * [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/Projects/alsharifmuhammad_100260_4748143_Alsharif_Project.ipynb|Learning to play Pong using Reinforcement Learning]] 
-  * [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/Projects/Christensen Final Project.ipynb|Reinforcement Learning for Solving the Traveling Salesman Problem]] 
-  * [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/Projects/Purandare Report.ipynb|Wholesale Customers Data Classification]] 
-  * [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/Projects/Overton Project.ipynb|Machine Learning Applied to Control Systems]] 
-  * [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/Projects/Baby_Weight_Prediction.ipynb|Prediction of Child's Birth Weight]] 
-  * [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/Projects/ReillyFinal.ipynb|Tensorflow for Reinforcement Learning]] 
-  * [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/Projects/Shaffer Final.ipynb|K Top Synonyms]] 
-  * [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/Projects/Project_text.ipynb|Predicting Shelter Animal Outcomes with Different Machine Learning Algorithms]] 
-  * [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/Projects/Larson Final.ipynb|Predicting NFL Scores by Weather]] 
-  * [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/Projects/Shah & Sudalaikkan Term Project.ipynb|Outlier Detection using Mahalanobis’ distance]] 
-  * [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/Projects/Xu Mitra Final Project Submission.ipynb|Training Flappy Bird Using Q-Learning]] 
-  * [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/Projects/hudavid_52971_4747929_Hu&Zhu FinalProjectPart1-2.ipynb|Finding the best training setting for GOMOKU for two levels of given system resources, Part 1]] and [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/Projects/hudavid_52971_4747930_Hu&Zhu FinalProjectPart2-2.ipynb|Part 2]] 
  
-*/ 
  
schedule.1481727168.txt.gz · Last modified: 2016/12/14 07:52 by anderson