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Meeting time and place: Lecture (001): 4:00 P.M. - 5:15 P.M. Tuesday/Thursday Fraser Hall 101.
|jparker (at) cs (dot) umn (dot) edu|
|Office||Shepherd Laboratories 391|
|Office Hours||Please see "Office Hours" on the class homepage (below).|
TAs: The TAs are primarily in charge of grading and office hours. Their names, contact information, and office hours are posted in the "Office Hours" page on the class homepage.
Text: Russel and Norvig, Artificial Intelligence A Modern Approach Pearson, 3rd edition. There may also be other resources linked to the class web page.
Class Website: There are two websites associated with this course: moodle and a public cse website. Moodle is primarily used to submit homework, see grades, talk on a forum and see announcements. The public cse webpage will have the schedule and any resources used in lecture. Please check the schedule frequently for any changes due to the pacing of the class. Important announcements will also be sent out through email (Labeled with "[CSci4511W]" in the title).
Prerequisites: Before taking this course, you should have:
General Course Description: This course covers the fundamentals of Artificial Intelligence (AI). We will cover: a brief overview, agent definition search (search space, uninformed and informed search, game playing, constraint satisfaction), planning, knowledge representation (logical encodings of domain knowledge, ontologies). Lisp will be used to a small extent and resources are posted on the website. This course will prepare you for more advanced AI topics, such as learning and more advanced modeling.
Writing Intensive Course: As this is a writing intensive course, you will learn how to write technically and succinctly. Feedback will be given on your writings and you will be allowed to resubmit them to improve your score (within two weeks of being returned).
For all graded work, please address any concerns within two weeks of receiving the grade. We will not change grades after two weeks. Here is the amount each of the items will contribute to your overall grade:
Homework 25% Writing assignments 20% Project 15% Midterm 1 (Week 6: Oct. 9) 10% Midterm 2 (Week 11: Nov. 13) 10% Final Exam (4:00-6:00 P.M., Tuesday, Dec. 18) 20% In-class activities (extra credit) 3%
Course Content and Components:
Grading for this course is on an absolute scale, so that the performance of others in the class will not negatively affect your grade. Final grades will be assigned based the following scale:
93.0% -- 100.0% A 90.0% -- 93.0% A- 87.0% -- 90.0% B+ 83.0% -- 87.0% B 80.0% -- 83.0% B- 77.0% -- 80.0% C+ 73.0% -- 77.0% C 70.0% -- 73.0% C- 67.0% -- 70.0% D+ 60.0% -- 67.0% D 0% -- 60.0% FFor S/N grading, a satisfactory grade (S) requires a weighted score of 70 or above.
Scholastic conduct: Both homework and written assignments are individual assesments (unless explicitly stated otherwise). This means your discussions about the questions can only cover the general approaches necessary to solve the problem. You should never share your work or see another student's answer to any part of the problem. It is also not allowed to work so closely together that your answers appear very similar, even if nothing was ever explicitly shared. If you have any questions about what is and is not allowable in this class, please ask the course instructor.
Disability Accommodations: We desire to make learning rewarding and fun for all students and make every attempt to accommodate anyone who has a desire to learn. If you require special classroom or test-taking accommodations, you need to contact the University Disability Services and also notify the instructor as soon as possible at the start of the semester (no later than 3 weeks prior to the first examination).