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syllabus

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Overview

Description

The course objectives are to learn the fundamental theories, algorithms and concepts in 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 programming assignments and a semester project.

We will be using Python for assignment solutions. Previous experience with Python and its numpy package is helpful. To prepare for this course, please download and install Python on your computer, and work through on-line tutorials to help prepare for this course. The 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.

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 your code must be written by you.

You are expected to be familiar with the CS Department policy on cheating and with the CS Department Code of Ethics. This course will adhere to the CSU Academic Integrity Policy as found in the General Catalog 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 Tuesday and Thursday, 2:00 PM - 3:15 PM, on-line as a Microsoft Teams meeting that you can find at this link. You may download Microsoft Teams apps for Windows, Mac, and Linux from this link at Microsoft.

Prerequisites

CS320 with a grade of C or better.

Textbook

Instructors

Office Hours Contact
Chuck Anderson Computer Science Building (CSB) Room 444 Wednesdays 1-3 PM
Microsoft Teams
chuck.anderson@colostate.edu
970-491-7491
GTA: ??
GTA: ??

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.

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, from 10% to 16% each
  • 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.

syllabus.1598217364.txt.gz · Last modified: 2020/08/23 15:16 by anderson