CS440: Introduction to Artificial Intelligence

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syllabus [2015/11/09 14:09]
anderson * 		syllabus [2016/05/04 11:06]
anderson [Grading]
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 [[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.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.
  
-=== Grading ===+===== Instructors =====
  
-Your grade for this course will be based only on the assignments, most of which will be written reports and submitted Python code. Each written report will require you to implement and run a machine learning algorithm and to write the report on your methods, results and conclusions. You must use python for the implementation and latex to make the report. Each report will be graded for correct implementation and results, interesting and thorough discussion, and good organization, grammar and spelling. Submitted code will be run and tested for correct functioning.+^    ^  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  | 
 +^  GTA: [[http://www.cs.colostate.edu/~lemin/|Jake Lee]]  |   |  Room 120\\ Wednesday 4 - 6 PM\\ Friday 2 - 4 PM  |  lemin@cs.colostate.edu  |
  
-We plan for 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. This 20% will be composed of+ 
 +===== Grading ===== 
 + 
 +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
   * 2% for the proposal   * 2% for the proposal
-  * 10% for the written report for on-campus students, 18% for distance-learning students +  * 18% for the written report
-  * 8% for the presentation by on-campus students+
  
 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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   * D 60 - 69.9%   * D 60 - 69.9%
   * F below 60%   * F below 60%
-  * +
 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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