CS440: Introduction to Artificial Intelligence

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schedule [2016/01/12 14:49]
anderson 		schedule [2016/01/26 14:03]
anderson
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 ====== Schedule ====== ====== Schedule ======
 +
 +Follow this link to view all [[https://echo.colostate.edu/ess/portal/section/37e115b6-e68b-4318-89ff-d1ecf025c0b9|lecture videos]].
 +
 +== Announcements ==
 +
 +Assignment 1 (A1) is now due Friday, January 29th, at 10 PM.
  
 == January == == January ==
  
 +|< 100% 20% 20% 20% 30% 10%  >|
 ^  Week      ^  Topic      ^  Material  ^  Reading          ^  Assignments  ^ ^  Week      ^  Topic      ^  Material  ^  Reading          ^  Assignments  ^
-| Week 1: Jan 19 - Jan 22    | Overview.     | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/01 Course Overview.ipynb|01 Course Overview]] |  Chapter 1 of textbook. 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    |      |       |+| 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]]   | 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]]  |  
  
 == February == == February ==
  
-^ Week      ^ Topic      ^ Reading          ^ Assignments  ^ +|< 100% 20% 20% 20% 30% 10%  >| 
-| Week 3: Feb 1 - Feb 5    |      |       | +^  Week      ^  Topic      ^  Material  ^  Reading          ^  Assignments  ^ 
-| Week 4: Feb 8 - Feb 12    |      |       | +| 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 5: Feb 15 - Feb 19    |      |       | +| Week 4:\\ Feb 8 - Feb 12    | Recurrent neural networks.     | | 11.9, 11.12, 11.14   | 
-| Week 6: Feb 22 - Feb 26    |      |       |+| Week 5:\\ Feb 15 - Feb 19    | Classification, generative models.   | | 4.3-4.5, 5.5-5.7  | 
 +| Week 6:\\ Feb 22 - Feb 26    | Classification, discriminant models.  Ranking.  | | 10.1-10.4, 10.5-10.10    |
  
 == March == == March ==
  
-^ Week      ^ Topic      ^ Reading          ^ Assignments  ^ +|< 100% 20% 20% 20% 30% 10%  >| 
-| Week 7: Feb 29 - Mar 5    |      |       | +^  Week      ^  Topic      ^  Material  ^  Reading          ^  Assignments  ^ 
-| Week 8: Mar 7 - Mar 11    |      |       | +| Week 7:\\ Feb 29 - Mar 5    | Classification with neural networks.     | | 11.7.2     | 
-|  Mar 14 - Mar 18    |  Spring Break!    |       | +| Week 8:\\ Mar 7 - Mar 11    | Convolutional, bottleneck, and deep networks.    | | 11.8.3, 11.11, 11.13     | 
-| Week 9: Mar 21 - Mar 25    |      |       | +|  Mar 14 - Mar 18    | Spring Break!    |       | 
-| Week 10: Mar 28 - Apr 1    |      |       |+| Week 9:\\ Mar 21 - Mar 25    | Nonparametric methods.  | | 8.1-8.10  | 
 +| Week 10:\\ Mar 28 - Apr 1    | Dimensionality reduction.  | | 6.1-6.8, 6.10-6.13  |
  
 == April == == April ==
  
-^ Week      ^ Topic      ^ Reading          ^ Assignments  ^ +|< 100% 20% 20% 20% 30% 10%  >| 
-| Week 11: Apr 4 - Apr 8    |      |       | +^  Week      ^  Topic      ^  Material  ^  Reading          ^  Assignments  ^ 
-| Week 12: Apr 11 - Apr 15    |      |       | +| Week 11:\\ Apr 4 - Apr 8    | Clustering  | | 7.1-7.10  | 
-| Week 13: Apr 18 - Apr 22    |      |       | +| Week 12:\\ Apr 11 - Apr 15    | Support vector machines.   | | 13.1-13.12   | 
-| Week 14: Apr 25 - Apr 29    |      |       |+| Week 13:\\ Apr 18 - Apr 22    | Reinforcement learning.   | | 18.1-18.9   | 
 +| Week 14:\\ Apr 25 - Apr 29    | Multiple models.    | | 17.1-17.12   |
  
 == May == == May ==
  
-^ Week      ^ Topic      ^ Reading          ^ Assignments  ^ +|< 100% 20% 20% 20% 30% 10%  >| 
-| Week 15: May 2 - May 6    |      |       |+^  Week      ^  Topic      ^  Material  ^  Reading          ^  Assignments  ^ 
 +| Week 15:\\ May 2 - May 6    |      |       |
  
schedule.txt · Last modified: 2024/01/08 18:40 (external edit)

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