CS440: Introduction to Artificial Intelligence

User Tools

Site Tools

Trace:
schedule

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
schedule [2016/05/10 08:46]
127.0.0.1 external edit 		schedule [2020/08/28 12:28]
anderson [Announcements]
Line 1: Line 1:
 ====== Schedule ====== ====== Schedule ======
- 
-Follow this link to view all [[https://echo.colostate.edu/ess/portal/section/37e115b6-e68b-4318-89ff-d1ecf025c0b9|lecture videos]]. 
  
 ===== Announcements ===== ===== Announcements =====
  
-**April 29:** My latest neural network code is available at [[http://www.cs.colostate.edu/~anderson/cs480/notebooks/nn7.tar|nn7.tar]].+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]] 
 + 
  
-===== January =====+Recordings of lecture and office hour videos are available from the Home page of our  
 +[[https://colostate.instructure.com/courses/109411|Canvas site]].
  
-|< 100% 20% 20% 30% 10% 20%  >| +To use jupyter notebooks on our CS department machines, you must add this line to your .bashrc file:
-^  Week      ^  Topic      ^  Material  ^  Reading          ^  Assignments  ^ +
-| Week 1:\\  Jan 19 - Jan 22    | 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 25 - Jan 29    | 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 =====+  export PATH=/usr/local/anaconda/bin:$PATH
  
-|< 100% 20% 20% 30% 10% 20%  >| +This is a tentative schedule of CS440 topics for Fall, 2020.  This will be updated during the summer and as the fall semester continues.
-^  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 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]]  | 11.1-11.5, 11.7.1, 11.7.4, 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]]   | 11.9, 11.12, 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 Monday, Feb 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 ===== 
  
-|< 100% 20% 20% 30% 10% 20%  >|+===== August ===== 
 + 
 +|< 100% 18% 20% 22% 20% 20%  >|
 ^  Week      ^  Topic      ^  Material  ^  Reading          ^  Assignments  ^ ^  Week      ^  Topic      ^  Material  ^  Reading          ^  Assignments  ^
-| 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 1:\\  Aug 24 - Aug 28    | What is AI?  Promises and fears.\\ Python review.\\ Problem-Solving Agents.  | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/01 Introduction to AI.ipynb|01 Introduction to AI]]\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/02 Introduction to Python.ipynb|02 Introduction to Python]]   | Chapters 1, 2, 3.1 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.I. Awakening]], 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 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     | +
-|  Mar 14 - Mar 18    | Spring Break!    |       | +
-| 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]]  | 11.8.3, 11.11, 11.13     |  +
-| 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 LDA, QDA, and Logistic Regression.ipynb|A4 Classification with LDA, QDA, and Logistic Regression]] due Tuesday, March 29 at 10:00 PM. Here 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]]  |+
  
-===== April =====+===== September =====
  
-|< 100% 20% 20% 30% 10% 20%  >|+|< 100% 18% 20% 22% 20% 20%  >| 
 +^  Week      ^  Topic      ^  Material  ^  Reading          ^  Assignments  ^nderson/cs545/doku.php?id=schedule#september 
 +| Week 2:\\ Aug 31 - Sept 4    | Problem-solving search and how to measure performance.\\ Iterative deepening and other uninformed search methods.   | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/03 Problem-Solving Agents.ipynb|03 Problem-Solving Agents]]\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/04 Measuring Search Performance.ipynb|04 Measuring Search Performance]] <!-- \\ [[http://nbviewer.ipython.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]]\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/06 Python Implementation of Iterative Deepening.ipynb|06 Python Implementation of Iterative Deepening]]  -->   | Sections 3.1 - 3.4 of Russell and Norvig  |   |  
 +| Week 3:\\ Sept 7 - Sept 11  | Informed search. A* search. Python classes, sorting, numpy arrays.  |   | Rest of Chapter 3  | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/A1 Uninformed Search.ipynb|A1 Uninformed Search]] due Tuesday, Sept. 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]]  --> | 
 +| Week 4:\\ Sept 14 - Sept 18   | A* optimality, admissible heuristics, effective branching factor.\\ Local search and optimization.  | <!-- [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/07 Informed Search.ipynb|07 Informed Search]]\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/08 Python Classes.ipynb|08 Python Classes]]\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/09 Heuristic Functions.ipynb|09 Heuristic Functions]]\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/10 Local Search.ipynb|10 Local Search]]  -->  | Chapter 4  |  <!-- [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/A2 Iterative-Deepening Search.ipynb|A2 Iterative-Deepening Search]] due Friday, Sept. 14, 10:00 PM.  Submit your notebook in Canvas.\\ Here are [[http://www.cs.colostate.edu/~anderson/cs440/notebooks/goodones|good solutions from your classmates]]  -->  | 
 +| Week 5:\\ Sept 21 - Sept 25   | Adversarial search. Minimax. Alpha-beta pruning. Stochastic games.  | <!-- [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/11 Adversarial Search.ipynb|11 Adversarial Search]]  -->  | Chapter 5  | 
 +| Week 6:\\ Sept 28 - Oct 2   | Negamax, with pruning. Introduction to Reinforcement Learning.  | <!-- [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/12 Negamax.ipynb|12 Negamax]]\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/13 Modern Game Playing.ipynb|13 Modern Game Playing]]\\  [[http://nbviewer.ipython.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]]   |  <!--  [[http://nbviewer.ipython.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 Wednesday, Sept. 26, 10:00 PM.  Submit your notebook in Canvas. -->   | 
 + 
 +===== October ===== 
 + 
 +|< 100% 18% 20% 22% 20% 20%  >|
 ^  Week      ^  Topic      ^  Material  ^  Reading          ^  Assignments  ^ ^  Week      ^  Topic      ^  Material  ^  Reading          ^  Assignments  ^
-| Week 11:\\ Apr 4 - Apr 8      | 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  | Reinforcement Learning for Two-Player Games.  | <!-- [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/14 Introduction to Reinforcement Learning.ipynb|14 Introduction to Reinforcement Learning]]\\ [[http://nbviewer.ipython.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  | Introduction to Neural Networks  | <!-- [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/16 Introduction to Neural Networks.ipynb|16 Introduction to Neural Networks]]\\ [[http://nbviewer.ipython.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  |   | 
-| 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.\\ 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  | More Neural Networks. Autoencoders.  | <!-- [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/17 More Introduction to Neural Networks.ipynb|17 More Introduction to Neural Networks]]\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/22 Autoencoder Neural Networks.ipynb|22 Autoencoder Neural Networks]] -->  | 
-| 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 Classification. Bayes Rule. Generative versus Discriminative. Linear Logistic Regression.  | <!-- [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/18 Introduction to Classification.ipynb|18 Introduction to Classification]] -->  |  | <!-- [[http://nbviewer.ipython.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 Monday, Oct. 22, 10:00 PM.  Submit your notebook in Canvas. -->  |  |
  
 +===== November =====
  
 +|< 100% 18% 20% 22% 20% 20%  >|
 +^  Week      ^  Topic      ^  Material  ^  Reading          ^  Assignments  ^
 +| Week 11:\\ Nov 2 - Nov 6  | Classification with Neural Networks. Reinforcement Learning with Neural Networks.  | <!-- [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/19 Classification with Linear Logistic Regression.ipynb|19 Classification with Linear Logistic Regression]]\\ [[http://nbviewer.ipython.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]]\\ [[http://nbviewer.ipython.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]]  -->  | |   |
 +| Week 12:\\ Nov 9 - Nov 13  | Introduction to Pytorch.\\ Constraint satisfaction.\\ Min-conflicts.  | <!-- [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/23 Introduction to Pytorch.ipynb|23 Introduction to Pytorch]]\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/24 Constraint Satisfaction Problems.ipynb|24 Constraint Satisfaction Problems]]\\ [[http://nbviewer.ipython.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\\ [[http://dl.acm.org/citation.cfm?id=1928809|A new iterated local search algorithm for solving broadcast scheduling problems in packet radio networks]]  | <!-- [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/A5 Neural Networks.ipynb|A5 Neural Networks]] due Monday, Nov. 5, 10:00 PM.\\ -->   |
 +| Week 13:\\ Nov 16 - Nov 20  | Natural language understanding and translation.   | <!-- [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/26 Natural Language.ipynb|26 Natural Language]]\\ [[http://nbviewer.ipython.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]]  |  |
 +|  Nov 23 - Nov 27  |  Fall Recess!  |
  
-===== May =====+===== December =====
  
-|< 100% 20% 20% 30% 10% 20%  >|+|< 100% 18% 20% 22% 20% 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  | Faster Reinforcement Learning   | <!-- [[http://www.cs.colostate.edu/~anderson/cs440/notebooks/15ijcnn.pdf|Slides for Faster Reinforcement Learning After Pretraining]] -->   | [[http://www.cs.colostate.edu/~anderson/res/rl/pretrainijcnn15.pdf|Faster Reinforcement Learning After Pretraining Deep Networks to Predict State Dynamics]] by Anderson, Lee and Elliott  | <!-- [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/A6 Min-Conflicts.ipynb|A6 Min-Conflicts]] due Wednesday, Nov. 28, 10:00 PM.  -->  |  
 +| Week 15:\\ Dec 7 - Dec 11  |   | <!-- **Dec 3:**\\ Tom Cavey: //Image Classification and Object Detection of Things Around CSU//\\ Jason Stock: //Classification of Data from the Sloan Digital Sky Survey//\\ Marylou Nash: //Physical Routing on ICs or PCBs with A*//\\  **Dec 5:**\\ Jake Walker: //Legal, Ethical, and Security Concerns for Autonomous Driving Technologies//\\ Andy Dolan: //Using Machine Learning Methods to Classify BGP Messages//\\ Miles Wood: //Using Q-Learning to Learn to Play Chad, a Chess Variant//\\ Apoorv Pandey: //Using Q-Learning to Learn to Play 2x2 Dots and Boxes//\\ **Dec 7:**\\ Markus Dabell: //Classification of Handwritten Digits from the MNIST Dataset//\\ Sajeeb Roy Chowdhury: //Searching for Optimal Schreier Trees in the Field of Combinatorics//\\ Mike Hamilton: //The Amazon AWS DeepRacer Platform for Reinforcement Learning Experimentation//  -->  |   |  
 +| Final Exam Week:\\ Dec 14 - Dec 18  |   |  | |   | 
 + 
 + 
  
-| 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  | 
  
schedule.txt · Last modified: 2024/01/08 18:40 (external edit)

Page Tools

Except where otherwise noted, content on this wiki is licensed under the following license: CC Attribution-Noncommercial-Share Alike 4.0 International
CC Attribution-Noncommercial-Share Alike 4.0 International Donate Powered by PHP Valid HTML5 Valid CSS Driven by DokuWiki