Neural Network Theory

Prof. Dr. Helmut Bölcskei

Offered in:



Basic information:

Lecture:Tuesday, 10:15-12:00, HG F 5.
Exercise session:   Tuesday, 12:15-13:00, HG F 5.
Instructors: Prof. Dr. Helmut Bölcskei
Teaching assistants: Yani Zhang, Valentin Abadie
Office hours:Thursday, 16:15-17:00 in ETF E 114. Please contact the TAs if you are planning to attend.
Lecture notes:The download link is provided below.
Recordings:Please note that recordings from past years will not be made available.
Credits: 4 ECTS credits
Course structure: The class will be taught in English. There will be a written exam in English of duration 180 minutes.


News

We will post important announcements, links, and other information here in the course of the semester.



Course Information

The class focuses on fundamental mathematical aspects of neural networks with an emphasis on deep networks.



Prerequisites

The course is aimed at students with a strong mathematical background in general, and in linear algebra, analysis, and probability theory in particular.



Lecture notes

Lecture notes




Problem sets and solutions

There will be several problem sets for this course, which will help you better understand the lectures and prepare you for the exam. All the problem sets will be discussed in the exercise session, and the solutions will be uploaded afterwards.

Problems Solutions
Set 1 Solution 1
Set 2 Solution 2
Set 3 Solution 3
Set 4 Solution 4
Set 5 Solution 5
Set 6 Solution 6
Set 7 Solution 7
Set 8 Solution 8
Set 9 Solution 9
Set 10 Solution 10
Set 11 Solution 11
Set 12 Solution 12
Set 13 Solution 13
Set 14 Solution 14


Previous exams and solutions

Winter Exam 2020: Problems Solutions
Summer Exam 2020: Problems Solutions
Winter Exam 2021: Problems Solutions
Summer Exam 2021: Problems Solutions
Winter Exam 2022: Problems Solutions
Summer Exam 2022: Problems Solutions
Winter Exam 2024: Problems Solutions