Course Identification

Title:

Topics in Machine Learning Theory

Code:

20254172

Lecturers and Teaching Assistants

Lecturers:

Dr. Gal Vardi

TA's:

Roey Magen, Daniel Barzilai

Course Schedule and Location

Year:

2025

Semester:

Second Semester

When / Where:

Monday, 14:15 - 16:00, Jacob Ziskind Building, Rm 155

Tutorials
Wednesday, 15:00 - 16:00, Goldsmith, Rm 108

First Lecture:

24/03/2025

End date:

30/06/2025

Field of Study, Course Type and Credit Points

Mathematics and Computer Science: Lecture; Elective; Regular; 3.00 points
Chemical Sciences: Lecture; 3.00 points
Life Sciences: Lecture; 3.00 points
Life Sciences (Brain Sciences: Systems, Computational and Cognitive Neuroscience Track): Lecture; 3.00 points

Comments

N/A

Prerequisites

No

Restrictions

Participants:

50

Language of Instruction

English

Attendance and participation

Expected and Recommended

Grade Type

Numerical (out of 100)

Grade Breakdown (in %)

Assignments:

40%

Final:

60%

Evaluation Type

Examination

Scheduled date 1

Date / due date

N/A

Location

N/A

Time

-

Remarks

N/A

Scheduled date 2

Date / due date

N/A

Location

N/A

Time

-

Remarks

N/A

Estimated Weekly Independent Workload (in hours)

3

Syllabus

This course will provide a self-contained introduction to the theory of machine learning and some of its currently-researched areas. It will be roughly divided into two parts: (1) Statistical learning theory; and (2) Theory of deep learning.

Important disclaimer: The course is theoretical in nature, aimed at students with a strong math/theoretical CS background, and focuses on theorems and proofs rather than machine learning applications.

Learning Outcomes

Upon successful completion of this course students should be able to:

Describe basic concepts, principles and algorithms in the field of machine learning theory.
Apply their acquired knowledge in their own areas of research.

Reading List

N/A

Website

N/A