This course gives a detailed overview of the fundamental concepts and recent advances in deep unsupervised learning where the topics covered include autoregressive models, normalizing flow models, variational autoencoders, generative adversarial networks, energy-based models, generative models with discrete latent variables, self-supervised learning, pretraining language models. The course is designed to familiarize students with the state-of-the-art in deep unsupervised learning by exposing them to a wide range of models and concepts in this new exciting field.
The course is taught by Aykut Erdem.
The course will use Deep Learning by Ian Goodfellow, Yoshua Bengio and Aaron Courville as the textbook (draft available online and for free here).
Instruction style: During the semester, students are responsible for studying and keeping up with the course material outside of class time. These may involve reading particular book chapters, papers or blogs and watching some video lectures. After the first three lectures, each week a student will present a paper related to the topics of the week.
Lectures: Mondays and Wednesdays at 08:30-09:45 (online via Zoom)
The course webpage will be updated regularly throughout the semester with lecture notes, presentations, assignments and important deadlines. All other course related communications will be carried out through Blackboard.
COMP201 is open to all COMP graduate students. Prospective senior undergraduate students may also enroll to the course but a consent is required. Similarly, non-COMP graduate students should also ask the course instructor for approval before the add/drop period. The prerequisites for this course are:
Grading for COMP547 will be based on
Week | Topic | Assignments |
Feb 15-17 | Introduction | |
Feb 22-24 | Neural Networks Basics | |
Mar 1-3 | Autoregressive Models | Assignment 1 out |
Mar 8-10 | Normalizing Flow Models | |
Mar 15-17 | Variational Autoencoders | Assg 1 due, Assg 2 out |
Mar 22-24 | Generative Adversarial Networks | Project proposal due |
Mar 29-31 | Energy-based Models | Assg 2 due, Assg 3 out |
Apr 5-7 | No classes - Spring Break | |
Apr 12-14 | Discrete Latent Variable Models | Assg 3 due, Assg 4 out |
Apr 19-21 | Project Progress Presentations | Project progress reports due |
Apr 26-28 | Self-Supervised Learning | Assg 4 due |
May 3-5 | Strengths and Weaknesses of Current Models | |
May 10-12 | Pretraining Language Models | |
May 17 | Final Project Discussions | |
May 24-26 | Midterm Exam | |
May 24-26 | Final Project Presentations | |
May 24-26 | Final Project Presentations |