CS440: Introduction to Artificial Intelligence

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schedule [2016/01/25 15:44]
anderson 		schedule [2016/02/22 12:57]
127.0.0.1 external edit
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 Follow this link to view all [[https://echo.colostate.edu/ess/portal/section/37e115b6-e68b-4318-89ff-d1ecf025c0b9|lecture videos]]. Follow this link to view all [[https://echo.colostate.edu/ess/portal/section/37e115b6-e68b-4318-89ff-d1ecf025c0b9|lecture videos]].
  
-== January ==+===== Announcements =====
  
-|< 100% 20% 20% 20% 30% 10%  >|+  * 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`. 
 + 
 +===== January ===== 
 + 
 +|< 100% 20% 20% 30% 10% 20%  >|
 ^  Week      ^  Topic      ^  Material  ^  Reading          ^  Assignments  ^ ^  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 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]]   | 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 Thursday, January 28th at 10:00 PM. Download and unzip [[http://www.cs.colostate.edu/~anderson/cs480/notebooks/A1 Grader.zip|A1 Grader.zip]]  |  +| 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 ==+===== February =====
  
-|< 100% 20% 20% 20% 30% 10%  >|+|< 100% 20% 20% 30% 10% 20%  >|
 ^  Week      ^  Topic      ^  Material  ^  Reading          ^  Assignments  ^ ^  Week      ^  Topic      ^  Material  ^  Reading          ^  Assignments  ^
-| Week 3:\\ Feb 1 - Feb 5    | Nonlinear regression with neural networks.    | | 11.1-11.5, 11.7.1, 11.7.4, 11.8.1-11.8.2  | +| 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    | Recurrent neural networks.     | | 11.9, 11.12, 11.14   | +| 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    | Classification, generative models.   | | 4.3-4.5, 5.5-5.7  | +| 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 6:\\ Feb 22 - Feb 26    | Classification, discriminant models.  Ranking.  | | 10.1-10.4, 10.5-10.10    |+| 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 =====
  
-|< 100% 20% 20% 20% 30% 10%  >|+|< 100% 20% 20% 30% 10% 20%  >|
 ^  Week      ^  Topic      ^  Material  ^  Reading          ^  Assignments  ^ ^  Week      ^  Topic      ^  Material  ^  Reading          ^  Assignments  ^
-| Week 7:\\ Feb 29 - Mar 5    | Classification with neural networks.     | | 11.7.2     | +| 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 8:\\ Mar 7 - Mar 11    | Convolutional, bottleneck, and deep networks.    | | 11.8.3, 11.11, 11.13     |+| Week 8:\\ Mar 7 - Mar 11     | Classification with neural networks.     | | 11.7.2     |
 |  Mar 14 - Mar 18    | Spring Break!    |       | |  Mar 14 - Mar 18    | Spring Break!    |       |
-| Week 9:\\ Mar 21 - Mar 25    | Nonparametric methods.  | | 8.1-8.10  | +| Week 9:\\ Mar 21 - Mar 25    | Convolutional, bottleneck, and deep networks.    | | 11.8.3, 11.11, 11.13     |  
-| Week 10:\\ Mar 28 - Apr 1    | Dimensionality reduction.  | | 6.1-6.8, 6.10-6.13  |+| Week 10:\\ Mar 28 - Apr 1    | Nonparametric methods.  | | 8.1-8.10  |
  
-== April ==+===== April =====
  
-|< 100% 20% 20% 20% 30% 10%  >|+|< 100% 20% 20% 30% 10% 20%  >|
 ^  Week      ^  Topic      ^  Material  ^  Reading          ^  Assignments  ^ ^  Week      ^  Topic      ^  Material  ^  Reading          ^  Assignments  ^
-| Week 11:\\ Apr 4 - Apr 8    | Clustering  | | 7.1-7.10  | +| Week 11:\\ Apr 4 - Apr 8      | Dimensionality reduction.  | | 6.1-6.8, 6.10-6.13  | 
-| Week 12:\\ Apr 11 - Apr 15    | Support vector machines.   | | 13.1-13.12   | +| Week 12:\\ Apr 11 - Apr 15    | Clustering  | | 7.1-7.10  |   
-| Week 13:\\ Apr 18 - Apr 22    | Reinforcement learning.   | | 18.1-18.9   | +| Week 13:\\ Apr 18 - Apr 22    | Support vector machines.   | | 13.1-13.12   | 
-| Week 14:\\ Apr 25 - Apr 29    | Multiple models.    | | 17.1-17.12   |+| Week 14:\\ Apr 25 - Apr 29    | Reinforcement learning.   | | 18.1-18.9   |
  
-== May ==+===== May =====
  
-|< 100% 20% 20% 20% 30% 10%  >|+|< 100% 20% 20% 30% 10% 20%  >|
 ^  Week      ^  Topic      ^  Material  ^  Reading          ^  Assignments  ^ ^  Week      ^  Topic      ^  Material  ^  Reading          ^  Assignments  ^
-| Week 15:\\ May 2 - May 6    |      |       |+| Week 15:\\ May 2 - May 6    | Multiple models.    | | 17.1-17.12   |
  
schedule.txt · Last modified: 2024/01/08 18:40 (external edit)

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