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--- syllabus [2015/11/09 14:17]
127.0.0.1 external edit
+++ syllabus [2017/07/03 11:07]
anderson [Time and Place]
@@ Line 3: / Line 3: @@
 ===== Description =====
-This course covers fundamental concepts and methods of computational data analysis, including pattern classification, prediction, visualization, and recent topics in deep learning. Students will learn how to
+The course objectives are to learn the fundamental theories,
+algorithms and representational structures underlying Artificial
+Intelligence.  Class discussions will range from algorithm
+fundamentals to philosophical issues in Artificial
+Intelligence. Programs implementing problem-solving search, logical
+reasoning. and machine learning techniques will be studied and
+modified. Other topics will be covered as time permits.  Students must
+complete a number of written and programming assignments and a
+semester project.  During the last week of class, semester projects
+will be presented by students.
-  * read data files of various formats and visualize characteristics of the data,
+We will be using [[https://www.python.org/|Python]] for assignment
-  * perform statistical analyses on multivariate data,
+solutions. You may download and install Python on your computer, and
-  * develop and apply pattern classification algorithms to classify multivariate data,
+work through the on-line tutorials to help prepare for this course.
-  * develop and apply regression algorithms for finding relationships between data variables,
+Experience with writing Python programs is not expected but helpful;
-  * develop and apply reinforcement learning algorithms for learning to control complex systems,
+an introduction to Python will be presented during the first few weeks
-  * write scientific reports on computational machine learning methods, results and conclusions.
+of the semester.
-For implementations we will be using [[https://www.python.org/|Python]]. You may download and install Python on your computer, and work through the on-line tutorials to help prepare for this course. For the written reports, we will be using [[https://www.latex-project.org/|LaTeX]], a document preparation system, freely available on all platforms.
+Class meetings will be a combination of lectures by the instructor and
+discussions of your questions.  You are expected to have read the
+assigned material before each class meeting. All questions are
+welcome, no matter how simple you think they are; it is always true
+that someone else has a similar question. Do not expect to be able to
+complete all assignments working on your own and not asking any
+questions. If you find yourself wondering what the next step is in
+finishing an assignment, visit or e-mail the instructor or the
+graduate teaching assistant. You may also discuss assignments with
+other students, but <color red/white>your code must be written by you</color>.
-Class meetings will be a combination of lectures by the instructor, discussions of students' questions, and some student presentations in class.
+You are expected to be familiar with the [[http://www.cs.colostate.edu/advising/student-info.html|CS Department policy on cheating]] and with the [[http://www.cs.colostate.edu/advising/CodeOfConduct.pdf|CS Department Code of Ethics]].
+This course will adhere to the CSU Academic Integrity Policy as found in the [[http://www.catalog.colostate.edu/FrontPDF/1.6POLICIES1112f.pdf|General Catalog]] and the [[http://www.conflictresolution.colostate.edu/conduct-code|Student Conduct Code]]. At a minimum, violations will result in a grading penalty in this course and a report to the Office of Conflict Resolution and Student Conduct Services.
 A lot of material will be covered in this course. Students are expected to speak up in class with questions and observations they have about the material. Do not expect to be able to complete all assignments working on your own and without asking any questions. If you find yourself wondering what the next step is in finishing an assignment, please feel free to e-mail the instructor. You may also discuss assignments with other students, but your code and report must be written by you.
-You are expected to be familiar with the [[http://www.cs.colostate.edu/advising/student-info.html|CS Department policy]] on cheating and with the [[http://www.cs.colostate.edu/cstop/csdepartment/CodeOfConduct.php|CS Department Code of Conduct]]. This course will adhere to the [[http://www.conflictresolution.colostate.edu/academic-integrity|CSU Academic Integrity Policy]]  and the Student Conduct Code. At a minimum, violations will result in a grading penalty in this course and a report to the Office of Conflict Resolution and Student Conduct Services.
 ===== Time and Place =====
-Class meets every Monday, Wednesday and Friday, 9:00 am - 9:50 in Clark Room A103.  On-campus and distance-learning students will be able to watch video recordings of lectures.
+Class meets every Tuesday and Thursday, 11:00 am - 12:15 am, in Room C111 in Aylesworth.  On-campus and distance-learning students will be able to watch video recordings of lectures.
 ===== Prerequisites =====
@@ Line 31: / Line 47: @@
 ===== Textbook =====
-=== Required ===
+Required: [[http://aima.cs.berkeley.edu/|Artificial Intelligence: A
+Modern Approach]], third edition. by
-[[http://www.cmpe.boun.edu.tr/~ethem/i2ml3e/|Introduction to Machine Learning]], by Ethem Alpaydin, 3rd edition, MIT Press, 2014.
+[[http://www.cs.berkeley.edu/~russell/|Stuart Russell]] and
+[[http://www.norvig.com/|Peter Norvig]].
-=== Optional ===
-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.
-[[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 ===
-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.  No quizzes or exams will be given.
+===== Instructors =====
-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
+^    ^  Office  ^  Hours  ^  Contact  |
-  * 2% for the proposal
+^  [[http://www.cs.colostate.edu/~anderson|Chuck Anderson]]  |  Computer Science Building (CSB) Room 444  |    Tuesdays 1-2, Thursdays 2-3  |  chuck.anderson@colostate.edu\\  970-491-7491  |
-  * 10% for the written report for on-campus students, 18% for distance-learning students
+^  GTAs: to be announced  |   |  Room 120\\ to be announced  |    |
-  * 8% for the presentation by on-campus students
-The calculation of the final letter grade will be made as follows:
-  * A 90 - 100%
+===== Grading =====
-  * 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.
+Details of the course grading policy will be posted here.
-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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