Logistics
Where: Zoom
When: Tuesday/Thursday 3:30 PM - 4:45 PM (CT)
Forums: Campuswire
Instructor: Yuxiong Wang
Office Hours: Monday 7:00 PM - 8:00 PM (CT) Zoom
TA: Rajbir Kataria, Yunze Man
Office Hours: Monday/Wednesday 6:00 PM - 7:00 PM (CT) Zoom
Practice presentation in TA OH
Assignments: Gradescope
Course Recording: Mediaspace
Updates
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Clarification: no paper reviews are required until the Sep 1 class, when we begin the presentations on specific topics.
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Aug 23: Course Begins. We will be using Campuswire for discussions. Please sign up if you haven't already done so. The passcode has been sent via email.
Overview
Summary: There has been a recent resurgence of interest in learning to learn, or meta-learning. In the standard machine learning paradigm, a model is trained on a set of examples and is specialized for the single task it is trained for. By contrast, meta-learning is performed on a set of tasks and leverages prior experiences when tackling a new task. This course will cover foundation principles, historical perspective, and recent progress of meta-learning. We will position meta-learning with respect to related areas, such as transfer learning, multi-task learning, and continual learning. The course will also discuss various applications of meta-learning in the fields of computer vision, natural language processing, reinforcement learning, and robotics. Students will be required to present and critique research papers and perform a related research project. By the end of the course, students will be able to understand and implement the state-of-the-art meta-learning algorithms and be ready to conduct research in this direction.
Prerequisites: While there are no formal prerequisites, this course assumes familiarity with machine learning (CS 446 or similar) and computer vision (CS 543 or similar). If you have not taken courses covering this material, consult with the instructor.
Awards: At the end of the course, we will vote for:
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Best Participation (Campuswire posts & In-class discussion)
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Best Presentation
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Best Project
People
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| Yuxiong Wang | Rajbir Kataria | Yunze Man |
| Instructor | TA | TA |
| yxw@illinois.edu | rk2@illinois.edu | yunzem2@illinois.edu |
Academic Integrity Policy
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The University of Illinois at Urbana-Champaign Student Code should also be considered as a part of this syllabus. Students should pay particular attention to Article 1, Part 4: Academic Integrity. Read the Code at the following URL: http://studentcode.illinois.edu/.
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Academic dishonesty may result in a failing grade. Every student is expected to review and abide by the Academic Integrity Policy: http://studentcode.illinois.edu/. Ignorance is not an excuse for any academic dishonesty. It is your responsibility to read this policy to avoid any misunderstanding. Do not hesitate to ask the instructor(s) if you are ever in doubt about what constitutes plagiarism, cheating, or any other breach of academic integrity.
Statement on CS CARES and CS Values and Code of Conduct
All members of the Illinois Computer Science department - faculty, staff, and students - are expected to adhere to the CS Values and Code of Conduct. The CS CARES Committee is available to serve as a resource to help people who are concerned about or experience a potential violation of the Code. If you experience such issues, please contact the CS CARES Committee. The instructors of this course are also available for issues related to this class.
Credits and Acknowledgment
I gratefully thank Abhinav Gupta, Lana Lazebnik, and Bo Li for borrowing much of their course design, thank Chelsea Finn, Sergey Levine, Hugo Larochelle, Oriol Vinyals, and Timothy Hospedales for their slides.