Course Identification

Title:

Topics in statistical modeling and inference

Code:

20254222

Lecturers and Teaching Assistants

Lecturers:

Prof. Boaz Nadler, Dr. Michael Feldman

TA's:

Eilon Vaknin

Course Schedule and Location

Year:

2025

Semester:

Second Semester

When / Where:

Sunday, 09:15 - 12:00, Ziskind, Rm 1

First Lecture:

23/03/2025

End date:

29/06/2025

Field of Study, Course Type and Credit Points

Mathematics and Computer Science: Lecture; Elective; Regular; 3.00 points
Chemical Sciences: 2.00 points
Life Sciences: 2.00 points

Comments

The exam should be submitted by August 18.

Prerequisites

No

Restrictions

Participants:

80

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

Take-home exam

Scheduled date 1

Date / due date

11/08/2025

Location

N/A

Time

-

Remarks

The exam should be submitted by August 18.

Estimated Weekly Independent Workload (in hours)

3

Syllabus

The course will cover several classical as well as modern topics in statistical inference, including:

- Statistical Modeling, parametric vs. non-parametric models

- The Maximum Likelihood Principle

- Non-parametric models, Kernel Density Estimation, Curse of Dimensionality

- High Dimensional statistics

- Sparsity

- Low rank models

- Spectral Methods

Learning Outcomes

Upon successful completion of this course students will be familiar with several classical and modern topics in statistics, the involved challenges, techniques to address them and their theoretical foundations.

Reading List

- Larry Wasserman, All of Statistics

- Martin Wainwright, High Dimensional Statistics

Website

N/A