General InformationLecture: 3:30-5:00pm, Tuesdays and Thursdays
Location: JGB 2.218 (in person or Zoom)
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
Weekly readings will be posted on the class website on Tuesday to be due the following week on Gradescope. 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;
- 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. 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 3:00 pm the day before the Tuesday class every week (aka 3:00 pm Monday). Responses received between then and 2 pm on the class day will be deducted 1 point (for a maximum score of 9). Responses received between then and 2 pm 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.
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 05/13, 2-5PM. It will be in-person at CAL 100.
You will be submitting a number of assignments, consisting of homework exercises, written responses to readings, and programming assignments. All submissions will be done through Gradescope.
Grading PolicyWritten responses to readings (5%)
Class participation (10%)
Homework exercises (20%)
Programming assignments (25%)
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 3:00 pm on Tuesday will have 5 points deducted if it is turned in late but before 3:00 pm on Thursday. It will have 6 points deducted if it is turned in by 3:00 pm Friday, etc.
The greater the advance notice of a need for an extension, the greater the likelihood of leniency.
Classroom Safety and COVID-19
To help preserve our in person learning environment, the university recommends the following.
- Adhere to university mask guidance.
- Vaccinations are widely available, free and not billed to health insurance. The vaccine will help protect against the transmission of the virus to others and reduce serious symptoms in those who are vaccinated.
- Proactive Community Testing remains an important part of the university’s efforts to protect our community. Tests are fast and free.
- Visit protect.utexas.edu for more information
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.