Seminar on Deep Learning Theory
Lecturers and Teaching Assistants
Prof. Ohad Shamir
Course Schedule and Location
Sunday, 10:00 - 12:00, Ziskind, Rm 1
Field of Study, Course Type and Credit Points
Mathematics and Computer Science: Seminar; Elective; Regular; 2.00 points
Life Sciences (Brain Sciences: Systems, Computational and Cognitive Neuroscience Track): Seminar; Elective; Regular; 2.00 points
Attendance and participation
Required in at least 80% of the lectures
Estimated Weekly Independent Workload (in hours)
In this seminar, students will read, present and discuss recent research papers on the theory of deep learning. The focus will be on rigorous results, pertaining to questions such as why neural networks are successfully trained with simple gradient based methods; why large-scale neural networks are able to generalize well; and how does the network's architecture affects the types of predictors it can express. Students will be able to choose a paper from a predefined list, or present a paper of their own choice (in coordination with the instructor).
Upon succesful completion of this course:
The students will gain familiarity with the current state-of-the-art in deep learning theory.