Mathematics of Deep Learning
Time: F 1:00-3:00 pm (10-04-19 to 12-06-19)

Place: Shaffer 300

Instructor: René Vidal (OH: F 3:00-4:00 pm, Clark 302B)

TA: Connor Lane (OH: Tu 4:00-5:00 pm, Clark 311A or B)

Course Description
The past few years have seen a dramatic increase in the performance of recognition systems thanks to the introduction of deep networks for representation learning. However, the mathematical reasons for this success remain elusive. For example, a key issue is that the training problem is nonconvex, hence optimization algorithms are not guaranteed to return a global minima. Another key issue is that while the size of deep networks is very large relative to the number of training examples, deep networks appear to generalize very well to unseen examples and new tasks. This course will overview recent work on the theory of deep learning that aims to understand the interplay between architecture design, regularization, generalization, and optimality properties of deep networks.
Class Schedule
Honor Policy
The strength of the university depends on academic and personal integrity. In this course, you must be honest and truthful. Ethical violations include cheating on exams, plagiarism, reuse of assignments, improper use of the Internet and electronic devices, unauthorized collaboration, alteration of graded assignments, forgery and falsification, lying, facilitating academic dishonesty, and unfair competition.