Course Description

Deep learning techniques now touch on data systems of all varieties. Sometimes, deep learning is a product; sometimes, deep learning optimizes a pipeline; sometimes, deep learning provides critical insights; sometimes, deep learning sheds light on neuroscience. The purpose of this course is to deconstruct the hype by teaching deep learning theories, models, skills, and applications that are useful for applications.

For a more extensive syllabus, see here.

Course Logistics

  • Instructor: Dr. Konrad Kording
  • Time and Location: Tuesdays & Thursdays 12-1:30pm, in Wu and Chen Auditorium (Levine 101)
  • Piazza:
  • Prerequisites: Must have a background in Machine Learning such as CIS 519 / CIS 520 / ESE 546 or equivalent. A background in linear algebra, probability and Python is also highly recommended.
  • Lecture Link (4/7): See @642