Instructor: Peter Stone
Department of Computer Sciences
office hour: Thursdays 11am-noon (please let me know in advance if you're coming) and by appointment
office: GDC 3.508
office hours: Tuesday 12-1pm, Wednesday 11am-noon
office: GDC 1.302 (Northside basement TA stations)
office hours: Monday 10-11am, Friday 12:30-1:30pm
office: GDC 1.302 (Northside basement TA stations)
office hours: Tuesday 3:30-4:30pm
office: GDC Basement Lab
Upper-division honors standing in CS.
Selected readings from this text will be assigned, possibly to be supplemented by relevant research papers.
Reading, written, and programming assignments will be updated on the
The readings and exercises may change up until the Tuesday before they are due (1 week in advance).
While the Professor and the TA would be glad to answer any questions you have, you will frequently find your peers to be an equally important resource in this class.
Please subscribe to our class piazza page.
There are three primary objectives for the course:
The course is designed to present a solid entry point to the field of artificial intelligence. It will provide the foundation to go on to take other upper division AI courses. For those students with interest, it could possibly lead to subsequent research opportunities.
There is no generally accepted definition of "artificial intelligence." Some that have been proposed include:
This course provides a broad introduction to artificial intelligence. Topics include:
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.
|Assignment||Date Assigned||Date Due|
|EdX Homework 1: Search||9-5||9-19|
|EdX Homework 2: Constraint Satisfaction||9-19||9-26|
|EdX Homework 3: Adverserial Search||9-26||10-3|
|EdX Homework 4: Markov Decision Processes||10-3||10-10|
|EdX Homework 5: Reinforcement Learning||10-10||10-17|
|EdX Homework 6: Probability and Bayes Nets||10-17||10-31|
|EdX Homework 7: Bayes Nets: Sampling, Decision Diagrams, VPI + HMMs, Particle Filtering||10-31||11-7|
|EdX Homework 8: Naive Bayes and Perceptrons||11-7||11-14|
|EdX Homework 9: Neural Networks, Optimization, and Deep Learning||11-14||11-28|
|Project 0: Python Tutorial||8-31||9-5|
|Project 1: Search||9-5||9-26|
|Project 2: Multi-agents||9-26||10-10|
|Project 3: Reinforcement Learning||10-10||11-2|
|Project 4: Bayes Nets||11-2||11-9|
|Project 5: Ghostbusters||11-9||11-21|
|Project 6: Classification||11-21||12-7|
|Contest: Capture the Flag||10-31||12-5|
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 12:30pm on Tuesday will have 5 points deducted if it is turned in late but before 12:30pm on Thursday. It will have 6 points deducted if it is turned in by 12:30pm Friday, etc.
The greater the advance notice of a need for an extension, the greater the likelihood of leniency.
You are encouraged to discuss the readings and concepts with classmates. But all written work must be your own. And programming assignments must be your own except for 2-person teams when teams are authorized. All work 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. If in doubt, look at the departmental guidelines and/or ask.
The University of Texas at Austin provides upon request appropriate academic accommodations for qualified students with disabilities. To determine if you qualify, please contact the Dean of Students at 471-6529; 471-4641 TTY. If they certify your needs, I will work with you to make appropriate arrangements.
A student who misses an examination, work assignment, or other project 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 complete satisfactorily the missed assignment or examination within a reasonable time after the excused absence.
Slides from the classes as well as other resources are posted on the class resources page.
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