Description

This course introduces the key concepts and methods underlying modern generative AI through a combination of lecture videos, in-class discussion, programming labs, and projects. Each topic will include a pre-recorded video with accompanying multiple-choice questions and/or small coding exercises which students are expected to review before each Lecture (typically on Mondays and Wednesdays; see Schedule below). On Fridays, students will participate in labs focused on hands-on application of the topics covered during the week. The course is divided into three modules, each spanning 5 weeks, with the last week containing a week-long group project.

This course will meet on Mondays, Wednesdays, and Fridays at 11:30am-12:20pm in the Wilmeth Active Learning Center (WALC) Room 3090.

We will primarily use Ed for class content, discussion, and announcements. If you are not in the Ed course, ask an instructor to be added.

Instructors and TAs

Abulhair Saparov

Instructor

Office Hours: After class

Fulya Gokalp Yavuz

Instructor

Office Hours: After class

Tania Chakraborty

TA

Office Hours: TBA

Nishanth Nakshatri

TA

Office Hours: TBA

Grading

• Labs: (20%) Every Friday, there will be a programming lab where students are tasked with implementing and experimenting with AI algorithms and models. The lowest lab grade will be dropped for each student.
• Projects: (60%) There will be three week-long projects throughout the course. Students will be required to work in groups and give a presentation at the end of the project. Each project is worth 20%.
• In-class Participation: (10%) Students are required to actively participate in and contribute to the class discussions and labs. Students should notify instructors if they expect to be absent for any of the discussions.
• Online Engagement: (10%) Students will be required to watch lecture videos before each Lecture class. These videos will be accompanied by short multiple-choice questions and coding exercises. We will keep track of whether students have watched the videos and completed the accompanying exercises, but we will not grade the correctness of student responses.

Schedule

Note the following schedule is subject to change throughout the course.

Late Policy

Labs are to be submitted by the due date listed. To provide some flexibility, we will drop the lowest-scoring lab from consideration when calculating your grade.

Academic Honesty

Please read the departmental academic integrity policy. This will be followed unless we provide written documentation of exceptions. We encourage you to interact amongst yourselves: you may discuss and obtain help with basic concepts covered in lectures or the textbook, homework specification (but not solution), and program implementation (but not design). However, unless otherwise noted, work turned in should reflect your own efforts and knowledge. Sharing or copying solutions is unacceptable and could result in failure. We use copy detection software, so do not copy code and make changes (either from the Web or from other students). You are expected to take reasonable precautions to prevent others from using your work.

Policy on Use of Generative AI

This course is meant to teach effective and responsible use of AI. For most of the course, unless explicitly permitted, students should not use AI tools to generate solutions, code, or written responses. We will actively encourage the use of AI tools after the lecture on Vibe Coding Methodology, in which case students are responsible for understanding and verifying the output and must be able to explain their work.