General InformationLecture: 9:30-11:00am, Tuesdays and Thursdays
Location: JGB 2.218
Discussion Forum: Piazza
Assignment Submissions: Gradescope
Recordings and Grades: Canvas
Course Textbook: Artificial Intelligence: A Modern Approach, by Russell and Norvig
Note: You need the 3rd Edition (Blue cover) published by Pearson
Students are expected to be present in class having completed the readings and participate actively in the discussions.
Throughout the semester, problem sets will be assigned and automatically graded through the Gradescope system. These homework exercises will be scored by the autograder. If you receive partial grades and would like to give it another try, you can come to the office hours to discuss these questions with the TAs. They will unlock you for resubmission. The goal of these problems is to get you comfortable with the material and prepared for the midterm and final.
There will be a series of Python programming projects in which you will implement various AI algorithms. An autograder script will be provided for each project so that you can check your progress along the way and fix errors in your code. The first of these projects (P0: Tutorial) must be completed individually. All other projects may be completed in pairs or alone.
A midterm exam will be given during a class session or as a take-home exam.
A final exam will be given on Friday, April 28th from 8-10 am. It will be in-person at GEA 105.
Grading PolicyClass participation (10%)
Homework exercises (20%)
Programming assignments (30%)
Extensions will be considered on a case-by-case basis, but in most cases they will not be granted. The greater the advance notice of a need for an extension, the greater the likelihood of leniency.
Programming AssignmentsYou are permitted to request a maximum of five extension days for the entire semester. No more than two extension days may be requested for any individual assignment with the exception of the first assignment for students who were added to the course.
You are encouraged to discuss assignments with classmates, but all collected data, analysis, images and graphs, and other written work must be your own. All programming assignments must be entirely your own except for teamwork on the final project. You may NOT look online for existing implementations of algorithms related to the programming assignments, even as a reference. Your code will be analyzed by automatic tools that detect plagiarism to ensure that it is original. For the final project, you have full access to the web, but all ideas, quotes, and code fragments that originate from elsewhere must be cited according to standard academic practice. Students caught cheating will automatically fail the course and will be reported to the university. If in doubt about the ethics of any particular action, look at the departmental guidelines and/or ask — ignorance of the rules will not shield you from potential consequences.
Notice about students with disabilities
The University of Texas at Austin provides upon request appropriate academic accommodations for qualified students with disabilities. For more information, contact the Division of Diversity and Community Engagement — Services for Students with Disabilities at 512-471-6529; 512-471-4641 TTY.
Notice about missed work due to religious holy days
A student who misses an examination, work assignment, or other projects due to the observance of a religious holy day will be given an opportunity to complete the work missed within a reasonable time after the absence, provided that he or she has properly notified the instructor. It is the policy of the University of Texas at Austin that the student must notify the instructor at least fourteen days prior to the classes scheduled on dates he or she will be absent to observe a religious holy day. For religious holy days that fall within the first two weeks of the semester, the notice should be given on the first day of the semester. The student will not be penalized for these excused absences, but the instructor may appropriately respond if the student fails to satisfactorily complete the missed assignment or examination within a reasonable time after the excused absence.