General Information

Lecture: 9:30-10:45am, Tuesdays and Thursdays
Location: Online (Check Canvas for Zoom links)
Discussion Forum: Please subscribe to our class Piazza page.
Assignments: Read the course requirements and assignment submission instructions
Course Textbook: Artificial Intelligence: A Modern Approach, by Russell and Norvig
Note: You need the 3rd Edition (Blue cover) published by Pearson

Course Requirements

Written Responses to Readings

Weekly readings will be posted on the class website on Tuesday to be due the following week on edX. Associated with some of the readings will be questions that should be answered with concise, well-thought-out, coherent written responses. In many cases, no specific questions will be posted. In those cases, the responses should be free form. Credit will be based on evidence that you have done the readings carefully. Acceptable responses include (but are not limited to):

  • Insightful questions;
  • Clarification questions about ambiguities;
  • Comments about the relation of the reading to previous readings;
  • Critiques;
  • Thoughts on what you would like to learn about in more detail;
  • Possible extensions or related studies;
  • Thoughts on the reading's importance; and
  • Summaries of the most important things you learned.

These responses will be graded on a 10-point scale with a grade of 9 being a typical full-credit grade. They will be graded mostly on coherence and evidence of careful thought (most questions will not have a "right" answer). Answers will be due by 09:30am the day before the Tuesday class every week (aka 09:30am Monday). Responses received between then and 8am on the class day will be deducted 1 point (for a maximum score of 9). Responses received between then and 8am the following class day will be deducted 2 points (for a maximum score of 8). Responses received after that will be deducted 4 points (for a maximum score of 6).

These deadlines are designed both to encourage you to do the readings before class and also to allow us to incorporate some of your responses into the class discussions.

Class Participation

Students are expected to be present in class having completed the readings and participate actively in the discussions.

Homework Exercises

Throughout the semester, problem sets will be assigned and automatically graded through the edX system. You will receive instant feedback from the autograder and can retry each problem as many times as necessary. The goal of these problems is to get you comfortable with the material and prepared for the midterm and final.

Programming Assignments

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 (TBA).

Submitting Assignments

You will be submitting a number of assignments, consisting of homework exercises, written responses to readings, and programming assignments.

For homework exercises, we will be using edX edge. Important: register for an edX edge account with your firstnamelastname as your username (no spaces, eg. johndoe). Then register for the course here.

For written responses to readings, we will release a separate Google Form each week.

You can find submission instructions for programming assignments here.

Grading Policy

Written responses to readings (5%)
Class participation (10%)
Homework exercises (20%)
Programming assignments (25%)
Midterm (15%)
Final (25%)

Extension Policy

If you turn in your assignment late, expect points to be deducted. No exceptions will be made for the written responses to readings-based questions (subject to the "notice about missed work due to religious holy days" below). For other assignments, extensions will be considered on a case-by-case basis, but in most cases they will not be granted.

For the penalties on responses to the readings see above (under course requirements). For other assignments, by default, 5 points (out of 100) will be deducted for lateness, plus an additional 1 point for every 24-hour period beyond 2 that the assignment is late. For example, an assignment due at 9:30am on Tuesday will have 5 points deducted if it is turned in late but before 9:30am on Thursday. It will have 6 points deducted if it is turned in by 9:30am Friday, etc.

The greater the advance notice of a need for an extension, the greater the likelihood of leniency.

COVID-19 Updates

Safety Information

While we will post information related to the contemporary situation on campus, you are encouraged to stay up-to-date on the latest news as related to the student experience. Check out this website for more information.

"Keep Learning" Resources

This course may be offered in a format to which you are unaccustomed. If you are looking for ideas and strategies to help you feel more comfortable participating in our class, please explore the resources available here.

Sharing of Course Materials is Prohibited

No materials used in this class, including, but not limited to, lecture hand-outs, videos, assessments (quizzes, exams, papers, projects, homework assignments), in-class materials, review sheets, and additional problem sets, may be shared online or with anyone outside of the class unless you have my explicit, written permission. Unauthorized sharing of materials promotes cheating. It is a violation of the University’s Student Honor Code and an act of academic dishonesty. We are well aware of the sites used for sharing materials, and any materials found online that are associated with you, or any suspected unauthorized sharing of materials, will be reported to Student Conduct and Academic Integrity in the Office of the Dean of Students. These reports can result in sanctions, including failure in the course.

Class Recordings

Class recordings are reserved only for students in this class for educational purposes and are protected under FERPA. The recordings should not be shared outside the class in any form. Violation of this restriction by a student could lead to Student Misconduct proceedings.

COVID Caveats

To help keep everyone at UT and in our community safe, it is critical that students report COVID-19 symptoms and testing, regardless of test results, to University Health Services, and faculty and staff report to the HealthPoint Occupational Health Program (OHP) as soon as possible. Please see this link to understand what needs to be reported. In addition, to help understand what to do if a fellow student in the class (or the instructor or TA) tests positive for COVID, see this University Health Services link.

Academic Integrity

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.