Autonomous AI

AIAA 5036, Spring 2023

Instructors: Junwei Liang


Home Schedule

Lecture:
Date and Time: 09:00AM - 11:50AM, Friday
Location: Rm 134, E1
Websites: Canvas

Course Description:
This course aims to provide students with key principles and algorithms to build modern autonomous AI systems. Key topics include machine perception, planning and decision-making algorithms. Through this course, students will learn and practice the foundational principles, techniques, and tools to build new autonomous AI systems.

Target Audience/ Prerequisites: This is a graduate course primarily for graduate students.

Course Work:
Grading      Grading will be based on bi-weekly reading assignments (50%) and a course project (45%). Note that 5% of your grade is assigned to attendance.
Reading Assignments
  • Reading assignments are designed to complement the lectures and showcase recent state-of-the-art research
  • There are 6 reading assignments over the semester, where you will typically have one weeks' time to work on each.
  • Most reading assignments will consist of 2 or 3 research papers. Each student is required to read only one research paper (out of the 2 or 3 assigned paper).
  • The reading assignments consist of two main parts: (1) submission of discussion post summarizing the paper you read that week (a summary, strengths, weaknesses), and (2) participation in the follow-up discussions. One question post to other summary or one answer to the question.
  • Each reading assignment is worth 10% credits: 8% for the paper summary and 2% for the extra posts in the discussion. We will keep your top 5 reading assignment scores.
  • Assignments and project results are worth full credit on the due date. 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.
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 with robotic systems which can then be scaled and generalized to other robotic systems and tasks.
  • Can be done in groups, defined at the beginning.
  • Topic ideas will be provided, but you can suggest your own (if suitable). The project should contain some level of autonomous AI component. Could be done in simulator or in real-world.
  • The project is worth 45% of your grade. These points are distributed as follows: 10% - Proposal Report (one-page); 15% - Final Project Presentation and Demo; 20% - Project report.
  • May involve virtual machines, AWS, Colab and Kaggle.

Instructors:
Junwei Liang
Rm 304, E4
Office hours: Friday 03:00PM - 04:00PM

Teaching Assistants:
TBD
TBD
Office hours: TBD
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.