Logistics
General Information
Course: CS 343H: Artificial Intelligence: HonorsUnique Number: 55340
Instructor: Ibrahim Volkan Isler (isler@cs.utexas.edu)
Teaching Assistant: Steve Wen (swen@cs.utexas.edu), office hours Tuesday 3:00–4:00 PM (online)
Lecture: Mon/Wed 02:00 PM - 03:30 PM
Location: ECJ 1.214
Instructional Mode: Face-to-face
Canvas: Class site
Prerequisites
The following coursework with a grade of at least C- in each: Computer Science 429 or 429H; and Mathematics 362K or Statistics and Data Sciences 321. Only one of Computer Science 343, 343H, or 378H (Topic: Artificial Intelligence: Honors) may be counted.
Course Requirements
Implementation Projects
Five implementation projects count for 35% of the course grade. Projects may be completed in pairs or alone. Project 0 is a required tutorial and must be completed individually. An autograder will be provided for each project.
Homework
Four homework assignments will be given. The lowest homework score will be dropped.
Quizzes
Four quizzes will be given, with the lowest quiz score dropped. Quizzes take place after the related homework submissions.
Final Exam
The cumulative final exam will take place in class on December 7. It will include questions similar in style, scope, and difficulty to the quizzes, plus one or two questions from the applications module.
Current assignment instructions, deadlines, and the detailed course timetable are posted on Canvas.
Grading Policy
| Component | Weight |
|---|---|
| Five implementation projects | 35% |
| Homework, top 3 of 4 | 21% |
| Quizzes, top 3 of 4 | 30% |
| Final exam | 14% |
Grade cutoffs: A 93%; A- 90%; B+ 86%; B 82%; B- 78%; C+ 74%; C 70%; C- 67%; D+ 63%; D 60%; F below 60%.
Make-up and Extension Policy
Projects
Students may request at most five extension days across the semester, with no more than two days for any individual project. An exception may be made for the first project when a student is added after its deadline.
Homework and Quizzes
There are no make-ups, extensions, or late submissions because the lowest homework and quiz scores are dropped.
Final Exam
A formal written note, such as documentation from a doctor's office, is required to make up the final exam.
Course Materials
Required: Reading assignments provided on Canvas.
Recommended: Artificial Intelligence: A Modern Approach, 4th US ed. The Berkeley CS 188 materials are also available.
Supplementary: Understanding Deep Learning for the final module.
Classroom and Academic Policies
No Electronics in the Classroom
Students are encouraged to take notes with a notebook and pencil. A tablet with an electronic pen is permitted, but keyboards are not allowed.
Academic Integrity and Generative AI
Academic misconduct incidents will be reported to the Dean of Students. Generative AI tools may be used on a limited basis to learn course material, but AI-generated code or text must not be submitted directly as a student's own work. Any permitted use must be properly attributed.
See the Student Conduct and Academic Integrity website for university standards.
Class Recordings and Instructional Materials
Class recordings and instructional materials are reserved for students in the course, are protected under FERPA, and must not be shared outside the class.
University Policies and Student Resources
Students who need accommodations should contact Disability and Access and provide their accommodation letter as early as possible.
Additional safety, support, accessibility, and university policy information is available on the University Policies and Resources for Students Canvas page.