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syllabus [2020/08/23 15:19] anderson [Instructors] |
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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 and visualize characteristics of the data, | + | We will be using [[https:// |
- | * perform statistical analyses on multivariate data, | + | solutions. Previous experience with Python |
- | * develop | + | and its numpy package is helpful. |
- | * develop and apply regression algorithms | + | download |
- | * develop | + | tutorials to help prepare for this course. |
- | | + | [[https:// |
+ | recommended, which is a free download for all platforms. | ||
+ | A quick review of Python will be presented in the first week | ||
+ | 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:// | ||
+ | policy on cheating]] and with the | ||
+ | [[http:// | ||
+ | Code of Ethics]]. | ||
+ | Integrity Policy as found in the | ||
+ | [[http:// | ||
+ | Catalog]] and the | ||
+ | [[http:// | ||
+ | Conduct Code]]. At a minimum, violations will result in a grading | ||
+ | penalty in this course and a report to the Office | ||
+ | Resolution | ||
- | 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 Tuesday and Thursday, 2:00 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/ | ||
+ | ===== Prerequisites ===== | ||
+ | |||
+ | CS320 with a grade of C or better. | ||
+ | |||
+ | ===== Textbook ===== | ||
+ | |||
+ | Required: [[http:// | ||
+ | Modern Approach]], third edition. by | ||
+ | [[http:// | ||
+ | [[http:// | ||
+ | |||
+ | |||
+ | ===== Instructors ===== | ||
+ | |||
+ | ^ ^ Office | ||
+ | ^ [[http:// | ||
+ | ^ GTA: Apoorv Pandey | | | ||
+ | ^ GTA: Chaitanya Roygaga | ||
+ | |||
+ | |||
+ | ===== Grading ===== | ||
+ | |||
+ | Your grade for this course will be based only on the assignments, | ||
+ | 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. | ||
+ | notebook will be graded for correct implementation and results, | ||
+ | interesting and thorough discussion, and good organization, | ||
+ | and spelling. No quizzes or exams will be given. | ||
+ | |||
+ | We plan for six to seven 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 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. | ||
- | ===== Time and Place ===== |