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syllabus [2018/06/05 13:15]
anderson [Instructors]
syllabus [2020/12/06 10:37] (current)
anderson [Grading]
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 The course objectives are to learn the fundamental theories, The course objectives are to learn the fundamental theories,
-algorithms and representational structures underlying Artificial+algorithms and concepts in Artificial
 Intelligence.  Class discussions will range from algorithm Intelligence.  Class discussions will range from algorithm
 fundamentals to philosophical issues in Artificial fundamentals to philosophical issues in Artificial
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 reasoning. and machine learning techniques will be studied and reasoning. and machine learning techniques will be studied and
 modified. Other topics will be covered as time permits.  Students must modified. Other topics will be covered as time permits.  Students must
-complete a number of written and programming assignments and a+complete a number of programming assignments and a
 semester project. semester project.
  
 We will be using [[https://www.python.org/|Python]] for assignment We will be using [[https://www.python.org/|Python]] for assignment
-solutions. You may download and install Python on your computer, and +solutions. Previous experience with Python 
-work through the on-line tutorials to help prepare for this course. +and its numpy package is helpful.  To prepare for this course, please 
-Experience with writing Python programs is not expected but helpful; +download and install Python on your computer, and work through on-line 
-an introduction to Python will be presented during the first few weeks+tutorials to help prepare for this course.  The 
 +[[https://www.anaconda.com/distribution/|Anaconda distribution]] is 
 +recommended, which is a free download for all platforms. 
 +A quick review of Python will be presented in the first week 
 of the semester. of the semester.
  
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 finishing an assignment, visit or e-mail the instructor or the finishing an assignment, visit or e-mail the instructor or the
 graduate teaching assistant. You may also discuss assignments with graduate teaching assistant. You may also discuss assignments with
-other students, but <color red/white>your code must be written by you</color>+other students, but <color red/white>your code must be written by you</color> 
  
-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]]. +You are expected to be familiar with the 
-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+[[http://www.cs.colostate.edu/advising/student-info.html|CS Department 
- +policy on cheating]] and with the 
-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.+[[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.
  
 ===== Time and Place ===== ===== Time and Place =====
  
-Class meets every Monday, Wednesday and Friday3:00 pm - 3:50 pmin Clark A 206 On-campus and distance-learning students will be able to watch video recordings of lectures.+Class meets every Tuesday and Thursday2:00 PM - 3:15 PM**on-line as 
 +a Microsoft Teams meeting** that you can find [[https://teams.microsoft.com/l/meetup-join/19%3a323d2d59a8f64282b836e440b8cb32d9%40thread.tacv2/1598126257845?context=%7b%22Tid%22%3a%22afb58802-ff7a-4bb1-ab21-367ff2ecfc8b%22%2c%22Oid%22%3a%22bcd6d782-40c2-430e-8091-fd9ebd260de7%22%7d|at this link]].  You may download Microsoft Teams apps for Windows, Mac, and Linux from [[https://docs.microsoft.com/en-us/microsoftteams/get-clients|this link at Microsoft]].
  
 +During the lecture please leave your microphone muted.  if you have a question or comment, feel free to interrupt the lecture by unmuting yourself and saying something like "Excuse me, I have a question" Questions and comments are always welcome!!  I cannot guarantee that I will notice comments that you type in the Chat box.
 ===== Prerequisites ===== ===== Prerequisites =====
  
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 ^    ^  Office  ^  Hours  ^  Contact  | ^    ^  Office  ^  Hours  ^  Contact  |
-^  [[http://www.cs.colostate.edu/~anderson|Chuck Anderson]]  |  Computer Science Building (CSB) Room 444  | Room 444\\ Tuesday, 1-2\\ Thursday, 2- |  chuck.anderson@colostate.edu\\  970-491-7491 +^  [[http://www.cs.colostate.edu/~anderson|Chuck Anderson]]  |  Computer Science Building\\ Room 444  |  Wednesdays\\ 10am\\  [[https://teams.microsoft.com/l/meetup-join/19%3a323d2d59a8f64282b836e440b8cb32d9%40thread.tacv2/1598288070646?context=%7b%22Tid%22%3a%22afb58802-ff7a-4bb1-ab21-367ff2ecfc8b%22%2c%22Oid%22%3a%22bcd6d782-40c2-430e-8091-fd9ebd260de7%22%7d|MS Teams link]]  |  Chuck.Anderson@colostate.edu\\  970-491-7491 
-^  GTA: ?     Room ? | Room 120\\ To be announced   ?  |+^  GTA:\\  [[https://www.linkedin.com/in/apoorvdp/|Apoorv Pandey]]     |  Mondays\\ 2:00 - 4:00 PM\\ [[https://teams.microsoft.com/l/meetup-join/19%3a323d2d59a8f64282b836e440b8cb32d9%40thread.tacv2/1598300599034?context=%7b%22Tid%22%3a%22afb58802-ff7a-4bb1-ab21-367ff2ecfc8b%22%2c%22Oid%22%3a%22bcd6d782-40c2-430e-8091-fd9ebd260de7%22%7d|MS Teams Link]]     Apoorv.Pandey@colostate.edu 
 +^  GTA:\\   [[https://www.linkedin.com/in/chaitanyaroygaga/|Chaitanya Roygaga]]  |   |  Fridays\\ 2:00 - 4:00 pm\\ [[https://teams.microsoft.com/l/meetup-join/19%3a323d2d59a8f64282b836e440b8cb32d9%40thread.tacv2/1598301087268?context=%7b%22Tid%22%3a%22afb58802-ff7a-4bb1-ab21-367ff2ecfc8b%22%2c%22Oid%22%3a%22bcd6d782-40c2-430e-8091-fd9ebd260de7%22%7d|MS Teams Link]]  |  Chaitanya.Roygaga@colostate.edu   |
  
  
 ===== Grading ===== ===== Grading =====
  
-Your grade for this course will be based only on the assignments, most of which will require the submission of a jupyter notebook that includes text descriptions of your methods, results and conclusions and the python code for defining and applying AI algorithms 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 six to eight assignments, all 
- +of which will require the submission of a jupyter notebook that 
-We plan for about 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.+includes python code and and its application to specified problems and data.  In addition, each notebook must include thorough discussions of methods, resultsand conclusions. Each 
 +notebook will be graded for correct implementation and results, 
 +interesting and thorough discussion, and good organization, grammar 
 +and spelling.
  
-  * 80% regular assignments, from 10% to 16% each +At the end of the semester, your lowest assignment grade will be dropped,  Your semester grade will be based on the average of your remaining 7 assignment grades. Each assignment will be equally weighted.
-  * 2% for the project proposal +
-  * 18% for the project written report+
  
-The calculation of the final letter grade, which will include + and -, will be based on the standard grading scheme, with A+, A, and A- being for grades of 90% and above, B+, B, and B- for grades between 80% and 90%, etc.  The minimum grade for each letter grade might be lowered, but will not be raised, based on the distribution of semester average grades for the class.+The calculation of the final letter grade, which will include + and -, 
 +will be based on the standard grading scheme, with A+, A, and A- being 
 +for grades of 90% and above, B+, B, and B- for grades between 80% and 
 +90%, etc.  The minimum grade for each letter grade might be lowered from the standard rubric, 
 +but will not be raised, based on the distribution of semester average 
 +grades for the class.
  
-Late assignment solutions will not be accepted, unless you make arrangements with the instructor at least two days before the due date.+Late assignment solutions will not be accepted, unless you make 
 +arrangements with the instructor at least two days before the due 
 +date.
  
syllabus.1528226151.txt.gz · Last modified: 2018/06/05 13:15 by anderson