CS294-158 Deep Unsupervised Learning Spring 2019
(website under construction...)
About: This course will cover two areas of deep learning in which labeled data is not required: Deep Generative Models and Self-supervised Learning. Recent advances in generative models have made it possible to realistically model high-dimensional raw data such as natural images, audio waveforms and text corpora. Strides in self-supervised learning have started to close the gap between supervised representation learning and unsupervised representation learning in terms of fine-tuning to unseen tasks. This course will cover the theoretical foundations of these topics as well as their newly enabled applications.
Instructors: Pieter Abbeel, Peter Chen, Jonathan Ho, Aravind Srinivas
Contact info: firstname.lastname@example.org
When: Wednesdays 5-8pm; first lecture on 1/23
Where: Moffitt Library 106
Tentative list of topics (much subject to change):
Generative adversarial networks, variational autoencoders, autoregressive models, flow models, energy based models, compression, self-supervised learning, semi-supervised learning.
Prerequisites: deep learning at level of getting an A+ in cs231n.stanford.edu
Q: How do I get into this course?
A: Please fill out this survey, which we will use for admissions.
Q: Can undergraduates take this course?
A: This course is targeted towards a PhD level audience. But certainly exceptional undergraduates could be good fits, too, and your ability to take this course is not directly affected by your grad/undergrad student status, but by things we measure in the survey.
Q: Is this a real course or a seminar?
A: This is a real course. Instructors will give most of the lectures (with likely a few guest lectures). There will be substantial homework. There will be a substantial final project.
Q: How will grading work?
A: Details to be determined. But we expect grades to be determined largely by 3-5 substantial homeworks + a final project.
Q: I already want to start learning now, what can I do, can you point me to some research papers maybe?
A: Certainly, here is a zip file with about 100 papers much on-topic for this course, happy readings!!