Course Syllabus

Coursework will consist of three kinds of assignments. Programming projects will be in Python. All programming projects (except P0) may be done in teams of two, or done solo. If working as a pair just submit once using gradescope by creating a team submission. Multiple written homeworks will be given throughout the semester and must be completed individually. Additionally, we will have two closed book, non-cumulative exams - a midterm and a final exam.

Prerequisites: This course has substantial elements of both programming and mathematics, because these elements are central to modern AI.

• CS 3500 (Prior programming experience is expected; although we don't expect that you know Python, we do expect you to be able to pick it up rapidly.)
• CS 3130 Engineering Probability and Statistics
• CS 4150 Algorithms

Expectations: You are expected to come to class prepared by reading the assigned sections of the book ahead of time.

Class Website: https://dsbrown1331.github.io/intro-ai-class/

Class Time: Tuesday and Thursday, 12:25 PM - 1:45 PM

Class Location: WEB L104

Instructor: Daniel Brown  Links to an external site.

Office Hours: Wed 2:00-3:00pm or by appointment

Location:  MEB 2172

It is best to contact me through Piazza, as this will correctly classify your email so that I'm less likely to miss it.

TA Office Hours:

TA office hours are available here

Textbook

Information on textbooks and readings is available on the textbook page.

Class Schedule

The class schedule is available on the class website

Exact dates are subject to change.

Grading

Overall grades will be determined from:

• Homework assignments (45%)
• Programming projects (20%)
• In Class Participation (10%)
• Exams (25%)

Assignments (homework and projects) must be turned in electronically by midnight on the listed due date. A penalty of 10% per day will be assessed. The weekend counts as one day.

You have 4 free late days (weekend counts as one late day) that you can use on any homework or programming assignment during the semester. If you want to use a late day, please clearly indicate this in your submission.

A student may petition the instructor for a re-grade of a portion or all of an assignment within one week of grade posting. It is important to note that the original grade will not be considered during the re-grade and the potential exists for a grade to improve, stay the same, or worsen as a result of the re-grade.

Communication Policies:

This term we will be using Piazza for class discussion. The system is highly catered to getting you help fast and efficiently from classmates, the TA, and myself. Rather than emailing questions to the teaching staff, I encourage you to post your questions on Piazza.

Policy on Generative AI:

Unless otherwise specified, the use of generative AI (for example, ChatGPT and Github Copilot) to produce text or code for any assignments will be considered academic misconduct. An exception to this is the use of grammar/spell-checkers. The nuance of other uses of modern generative AI tools will be discussed in class.

This syllabus is meant to serve as an outline and guide for the course. Please note that your instructor may modify it to accommodate the needs of your class.

You will be notified of any changes to the Syllabus.

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Additional Resources and Policies:

Student Mental Health Resources

• Rates of burnout, anxiety, depression, isolation, and loneliness have noticeably increased during the pandemic. If you need help, reach out for campus mental health resources, including counseling, trainings and other support.
• Consider participating in a Mental Health First Aid or other wellness-themed training provided by our Center for Student Wellness and sharing these opportunities with your peers, teaching assistants and department colleagues.

Kahlert School of Computing Policies and Guidelines: https://handbook.cs.utah.edu/