Spring 2021: Advanced Machine Learning [CS 782]

General Description and Preliminary List of Topics
This course covers recent advances of machine learning. We will study a number of research papers.
Topics include: subspace clustering; clustering ensembles; metric learning; manifold learning; nonnegative matrix factorizaion; learning from graphs; transfer learning.
This list is tentative and is subject to change based on available time.

Course Format
Lectures by the instructor and presentations by students. Research papers, slides, and handouts will be made available. Grading is based on quizzes, presentations, participation, and a project. Graded work must be done on an individual basis, unless otherwise stated by the instructor. Any deviation from this policy will be considered a violation of the GMU Honor Code.

Online Classroom Specifics
Attendance is required. Lectures will be recorded, but students are required to attend the syncronous lecture. Students are expected to read the papers being presented on a given day, ask questions during lectures, and add to the discussions. Students' active participation is very important to succed in this course. We will also have quizzes synchronously. Specifics on the online platform will be communicated soon.

Communication
We'll be using Piazza for communication, and to enable questions and students' discussions.

Grading Policy
Participation: 20%
Paper Presentations: 10%
Quizzes: 35%
Project: 35%

Honor Code
This class enforces the GMU Honor Code and the more specific honor code policy special to the Department of Computer Science. You will be expected to adhere to this code and policy.

Disabilities
If you have a documented learning disability or other condition which may affect academic performance, make sure this documentation is on file with the Office of Disability Services and talk to me about accommodations.