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syllabus [2020/07/21 16:11] anderson |
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===== Description ===== | ===== Description ===== | ||
- | This course | + | The course |
+ | algorithms | ||
+ | Intelligence. Class discussions | ||
+ | fundamentals | ||
+ | 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. | ||
+ | complete a number of programming assignments and a | ||
+ | semester project. | ||
- | * read data files of various formats | + | We will be using [[https:// |
- | * perform statistical analyses | + | solutions. You may download |
- | * develop and apply pattern classification algorithms | + | work through |
- | * develop and apply regression algorithms | + | Experience with writing Python programs is not expected but helpful; |
- | * develop and apply reinforcement learning algorithms for learning | + | an introduction |
- | * write scientific reports on computational machine learning methods, results and conclusions. | + | of the semester. |
- | For implementations we will be using [[https:// | + | Class meetings |
+ | 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 | ||
+ | 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/ | ||
- | Class meetings will be a combination | + | You are expected to be familiar with the [[http:// |
+ | This course will adhere to the CSU Academic Integrity Policy as found in the [[http:// | ||
- | A lot of material will be covered in this course. Students are expected to speak up in class with questions | + | ===== Time and Place ===== |
- | You are expected to be familiar with the [[http:// | + | Class meets every Monday, Wednesday and Friday, 3:00 pm - 3:50 pm, in Clark A 202. On-campus |
+ | ===== Prerequisites ===== | ||
+ | |||
+ | CS320 with a grade of C or better. | ||
+ | |||
+ | ===== Textbook ===== | ||
+ | |||
+ | Required: [[http:// | ||
+ | Modern Approach]], third edition. by | ||
+ | [[http:// | ||
+ | [[http:// | ||
+ | |||
+ | |||
+ | ===== Instructors ===== | ||
+ | |||
+ | ^ ^ Office | ||
+ | ^ [[http:// | ||
+ | ^ GTA: Wen Qin | Room 415, Desk 11 | Room 120\\ Monday, Wednesday, 4-6 pm | wen.qin@colostate.edu | ||
+ | ^ GTA: Mohamed Chaabane | ||
+ | |||
+ | |||
+ | ===== Grading ===== | ||
+ | |||
+ | Your grade for this course will be based only on the assignments, | ||
+ | |||
+ | 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. | ||
+ | |||
+ | * 80% regular assignments, | ||
+ | * 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. | ||
+ | |||
+ | Late assignment solutions will not be accepted, unless you make arrangements with the instructor at least two days before the due date. | ||
- | ===== Time and Place ===== |