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--- syllabus [2015/11/09 12:57]
anderson
+++ syllabus [2016/12/14 07:40]
anderson [Textbook]
@@ Line 27: / Line 27: @@
 ===== Prerequisites =====
-CS320
+CS320 with a grade of C or better.
 ===== Textbook =====
-=== Required ===
+There are no required text books for this course.  Readings may be assigned from the following on-line books.
-[[http://www.cmpe.boun.edu.tr/~ethem/i2ml3e/|Introduction to Machine Learning]], by Ethem Alpaydin, 3rd edition, MIT Press, 2014.
+[[http://www.deeplearningbook.org/|Deep Learning]] by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
+[[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.
-=== Optional ===
+[[http://shop.oreilly.com/product/0636920023784.do|Python for Data Analysis]], by Wes Kinney, O'Reilly Media, Inc., 2013.
-On-line material is available on the course [[Resources]] web page.  Other books that may be helpful are listed here.
-[[http://shop.oreilly.com/product/0636920023784.do|Python for Data Analysis]], by Wes Kinney, O'Reilly Media, Inc., 2013.
+===== Instructors =====
+^    ^  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  |
+===== Grading =====
-[[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.
+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
+  * 18% for the written report
+The calculation of the final letter grade will be made as follows:
+  * A 90 - 100%
+  * B 80 - 89.9%
+  * C 70 - 79.9%
+  * D 60 - 69.9%
+  * F below 60%
+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.

CS440: Introduction to Artificial Intelligence

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