CS440: Introduction to Artificial Intelligence

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syllabus [2016/12/14 07:38]
anderson [Textbook] 		syllabus [2017/01/01 14:47]
anderson [Grading]
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 [[http://www.deeplearningbook.org/|Deep Learning]] by Ian Goodfellow, Yoshua Bengio, and Aaron Courville [[http://www.deeplearningbook.org/|Deep Learning]] by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
  
-[[http://webdocs.cs.ualberta.ca/~sutton/book/the-book.html| Reinforcement Learning: An Introduction]], by Richard Sutton and Andrew Barto. On-line and free. You can also read the book through Morgan library. Visit [[http://catalog.library.colostate.edu/search~S5?/treinforcement+learning/treinforcement+learning/1%2C12%2C16%2CB/frameset&FF=treinforcement+learning+an+introduction&1%2C%2C3|this page]] and click on the “View electronic book” link. +[[http://webdocs.cs.ualberta.ca/~sutton/book/the-book-2nd.html| Reinforcement Learning: An Introduction]], by Richard Sutton and Andrew Barto. 2nd edition. On-line and free.
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-[[http://shop.oreilly.com/product/0636920023784.do|Python for Data Analysis]], by Wes Kinney, O'Reilly Media, Inc., 2013.+
  
 +[[http://www.labri.fr/perso/nrougier/from-python-to-numpy/|From Python to Numpy]] by Nicolas P. Rougier 
  
 ===== Instructors ===== ===== Instructors =====
  
 ^    ^  Office  ^  Hours  ^  Contact  | ^    ^  Office  ^  Hours  ^  Contact  |
-^  [[http://www.cs.colostate.edu/~anderson|Chuck Anderson]]  |  Computer Science Building (CSB) Room 444  |    Monday 1-2, Wednesday 2-3  |  anderson@cs.colostate.edu\\  970-491-7491  |+^  [[http://www.cs.colostate.edu/~anderson|Chuck Anderson]]  |  Computer Science Building (CSB) Room 444  |    Monday 1-2, Wednesday 2-3  |  chuck.anderson@colostate.edu\\  970-491-7491  |
 ^  GTA: [[http://www.cs.colostate.edu/~lemin/|Jake Lee]]  |   |  Room 120\\ Wednesday 4 - 6 PM\\ Friday 2 - 4 PM  |  lemin@cs.colostate.edu  | ^  GTA: [[http://www.cs.colostate.edu/~lemin/|Jake Lee]]  |   |  Room 120\\ Wednesday 4 - 6 PM\\ Friday 2 - 4 PM  |  lemin@cs.colostate.edu  |
  
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 Your grade for this course will be based only on the assignments, most of which will require the submission of an ipython notebook that includes text descriptions of your methods, results and conclusions and the python code for defining machine learning algorithms, loading data and applying your algorithms to the data.  Each notebook will be graded for correct implementation and results, interesting and thorough discussion, and good organization, grammar and spelling.  No quizzes or exams will be given. Your grade for this course will be based only on the assignments, most of which will require the submission of an ipython notebook that includes text descriptions of your methods, results and conclusions and the python code for defining machine learning algorithms, loading data and applying your algorithms to the data.  Each notebook will be graded for correct implementation and results, interesting and thorough discussion, and good organization, grammar and spelling.  No quizzes or exams will be given.
  
-We plan for five regular assignments during the semester. In total these will count for 80% of your semester grade. The final assignment is a project designed by you and is worth 20% of your semester grade. This 20% will be composed of +We plan for five or six regular assignments during the semester. In total these will count for 80% of your semester grade. The final assignment is a project designed by you and is worth 20% of your semester grade. These percents are summarized in the following list. 
-  * 2% for the proposal +  * 80% regular assignments, from 10% to 16% each 
-  * 18% for the written report+  * 2% for the project proposal 
 +  * 18% for the project written report
  
 The calculation of the final letter grade will be made as follows: The calculation of the final letter grade will be made as follows:
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 These ranges for a letter grade might be shifted a little lower, but will not be raised. These ranges for a letter grade might be shifted a little lower, but will not be raised.
-Late reports will not be accepted, unless you make arrangements with the instructor at least two days before the due date.+**Late reports will not be accepted**, unless you make arrangements with the instructor at least two days before the due date.
syllabus.txt · Last modified: 2020/12/06 10:37 by anderson

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