CS440: Introduction to Artificial Intelligence

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schedule [2016/02/22 16:19]
anderson [Announcements] 		schedule [2016/05/10 08:46]
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 ===== Announcements ===== ===== Announcements =====
  
-  * Feb 22: [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/A3 Neural Network Regression.ipynb|A3 Neural Network Regression]], now includes link to ''A3grader.tar'' that contains ''A3grader.py''.+**April 29:** My latest neural network code is available at [[http://www.cs.colostate.edu/~anderson/cs480/notebooks/nn7.tar|nn7.tar]].
  
 ===== January ===== ===== January =====
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 |< 100% 20% 20% 30% 10% 20%  >| |< 100% 20% 20% 30% 10% 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 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 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.   | +| 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  |+| 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 ===== ===== March =====
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 |< 100% 20% 20% 30% 10% 20%  >| |< 100% 20% 20% 30% 10% 20%  >|
 ^  Week      ^  Topic      ^  Material  ^  Reading          ^  Assignments  ^ ^  Week      ^  Topic      ^  Material  ^  Reading          ^  Assignments  ^
-| Week 7:\\ Feb 29 - Mar 5     | Classification, discriminant models.  Ranking.  | | 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.  | +| 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 8:\\ Mar 7 - Mar 11     | Classification with neural networks.     | | 11.7.2     |+| 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!    |       | |  Mar 14 - Mar 18    | Spring Break!    |       |
-| Week 9:\\ Mar 21 - Mar 25    | Convolutional, bottleneck, and deep networks.    | | 11.8.3, 11.11, 11.13     |  +| 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    | Nonparametric methods.  | | 8.1-8.10  |+| 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 ===== ===== April =====
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 |< 100% 20% 20% 30% 10% 20%  >| |< 100% 20% 20% 30% 10% 20%  >|
 ^  Week      ^  Topic      ^  Material  ^  Reading          ^  Assignments  ^ ^  Week      ^  Topic      ^  Material  ^  Reading          ^  Assignments  ^
-| Week 11:\\ Apr 4 - Apr 8      | Dimensionality reduction.  | | 6.1-6.8, 6.10-6.13  | +| 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 12:\\ Apr 11 - Apr 15    | Clustering  | | 7.1-7.10  |   +| 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 13:\\ Apr 18 - Apr 22    | Support vector machines.   | | 13.1-13.12   | +| 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 14:\\ Apr 25 - Apr 29    | Reinforcement learning.   | | 18.1-18.9   |+| 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]]  | | 
 + 
  
 ===== May ===== ===== May =====
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 ^  Week      ^  Topic      ^  Material  ^  Reading          ^  Assignments  ^ ^  Week      ^  Topic      ^  Material  ^  Reading          ^  Assignments  ^
-| Week 15:\\ May 2 - May 6    | Multiple models.    | | 17.1-17.12   |+| 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 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)

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