Course Identification

Seminar on Machine Learning Theory
20254201

Lecturers and Teaching Assistants

Prof. Ohad Shamir
N/A

Course Schedule and Location

2025
First Semester
Sunday, 09:15 - 11:00, Ziskind, Rm 1
03/11/2024
26/01/2025

Field of Study, Course Type and Credit Points

Mathematics and Computer Science: Seminar; Elective; Regular; 2.00 points

Comments

N/A

Prerequisites

No

Restrictions

26

Language of Instruction

English

Attendance and participation

Required in at least 80% of the lectures

Grade Type

Pass / Fail

Grade Breakdown (in %)

20%
80%

Evaluation Type

Seminar

Scheduled date 1

N/A
N/A
-
N/A

Estimated Weekly Independent Workload (in hours)

3

Syllabus

In this seminar, students will read, present and discuss recent research papers in theoretical machine learning, in particular deep learning and related topics. The focus will be on rigorous results, pertaining to questions such as why predictors can be successfully trained with simple gradient based methods; why large-scale models (e.g. modern neural networks) are able to generalize well; and how does the predictor's architecture affects the types of functions 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). 

Learning Outcomes

Upon succesful completion of this course:

The students will gain familiarity with the current state-of-the-art in machine learning theory.

Reading List

N/A

Website

N/A