Foundations of Artificial Intelligence

AIAA 5032, Spring 2025

Instructors: Junwei Liang


Home Schedule

Lecture:
Date and Time: 09:00AM - 11:50AM, Thursday
Location: Rm 202, E3
Websites: Canvas
Zoom: 628-334-1826 PW: 12345 (In-person expected)

Course Description:
This course aims to provide students with an overview of Artificial Intelligence principles and techniques. Key topics include machine learning, neural networks, computer vision, reinforcement learning, large language models, etc. Intended learning outcome: 1. Students will learn to describe key principles, concepts, models and techniques in AI. 2. Students will have a broad understanding of what AI can accomplish today and what cannot 3. Students can implement AI algorithms introduced in class to solve small real-world problems. 4. Students can develop AI solutions to real-world problems through a course project and deliver written and oral presentations.

Target Audience/ Prerequisites: This is a graduate course primarily for graduate students. Basic Python programming and linear algebra knowledge is required.

Course Work:
Grading      Grading will be based on three assignments (40%) and a course project (50%). 10% of your grade is assigned to attendance.
Assignments
  • The goal of the assignments is to make sure that the fundamentals of AI are understood by all participants.
  • There are 2 homework assignments over the semester, where you will typically have two weeks' time to work on each. (Tentative credit distribution: 20%/20%)
  • Submission will be on Kaggle and Canvas.
  • We will post performance cutoffs for HIGH and OK for Kaggle competitions. There will be a leaderboard for each assignment to encourage trying extra things. Submissions above OK will get full credit. Submissions above HIGH will get an extra 2% credit for each assignment. The grade cutoff will be released 1 week before the DDL so you do not need to “内卷” till the last minute.
  • Homework assignments and course project results are worth full credit on the due date. This means you need to submit on the leaderboard and on Canvas (usually several days later than the Kaggle DDL) on time. Unless granted an extension in advance, it is worth at most 75% credit for the next 48 hours, at most 50% credit after that. If you need an extension, please ask for it as soon as the need for it is known. Extensions that are requested promptly can be granted more liberally. You must turn in all assignments.
  • Each homework is an individual assignment. You should not look at others' code.
Project
  • The goal of the course project is to define and perform a small-scale experiment on your own, in order to gain hands-on experience which can then be scaled and generalized to other AI tasks.
  • Can be done in groups, defined at the beginning.
  • Topic ideas will be provided, but you can suggest your own (if suitable)
  • Your project may relate to research in another class project, provided that instructors of both classes grant consent. There should be no more than a 50% overlap.
  • The project is a key component of this course. This is also intended to be a "flipped-classroom" experience, where you will have the opportunity to present how you define and solve a problem using AI to the class. The course instructors, the TAs and your peers will provide you with useful feedback.
  • The project is worth 50% of your grade. These points are distributed as follows: 10% - Project Proposal; 20% - Project Presentation (midterm and final); 20% - Project report. The grading rubrics will be described in the lecture.
Attendance and Final Grade
  • The attendance is worth 10%, which will be based on the three project presentation attendance and some other secret observations.
  • Final grade example: If you attend most of the lectures, have 2 OKs for the assignments and do a decent job in the project, you should get a grade of no less than A. (This means you have fulfilled the requirements of this course and achieved the intended learning outcome.). Having missed one assignment, you can only get at most B.

Instructors:
Junwei Liang
Rm 304, E4
Office hours: Friday 04:00PM - 05:00PM (Please let me know beforehand if you are coming)

Teaching Assistants:
Zeying Gong, Zifan Wang, Ronghe Qiu
zgong313@connect.hkust-gz.edu.cn
zwang765@connect.hkust-gz.edu.cn
rqiu683@connect.hkust-gz.edu.cn
Collaboration among Students: We encourage collaboration between students and studying materials in groups when the purpose of this is to facilitate learning, not to circumvent problems. It is allowed to seek help from other students in understanding the material needed to solve a particular problem. However, students must submit individual material and solutions, unless otherwise specified. Students should declare any collaboration on the first page of homework assignments (or equivalently on exercises). If the instructors believe the collaboration is improper, your grade may be affected.

Using ChatGPT and alike: We encourage you to use ChatGPT to solve the homework assignment problems (because likely in the future everyone will use it for coding). However, you will need to declare using ChatGPT/LLMs in the homework write-up, as well as the prompts you use