Mathematical Introduction to Deep Neural Networks

Professor: JProf. Dr. Diyora Salimova
E-Mail Professor: diyora.salimova@mathematik.uni-freiburg.de
Lecture: Wedndesday 12-14, SR 226, Hermann-Herder-Str. 10
Office Hours: by arrangement, R 208, Hermann-Herder-Str. 10
Exercises: Monday 16-18, SR 226, Hermann-Herder-Str. 10
Assistant: M.Sc. Ilkhom Mukhammadiev
E-Mail Assistant: ilkhom.mukhammadiev@mathematik.uni-freiburg.de
Office Hours: by arrangement, R 210, Hermann-Herder-Str. 10

Updates

Dates for an oral exam are 12.02.2026 (Thursday) and 25.02.2026 (Wednesday). Incase you want to take an oral exam please choose one of the dates and contact the lecturer to fix the exact time slot.

Content

The course will provide an introduction to deep learning algorithms with a focus on the mathematical understanding of the objects and methods used. Essential components of deep learning algorithms will be reviewed, including different neural network architectures and optimization algorithms. The course will cover theoretical aspects of deep learning algorithms, including their approximation capabilities, optimization theory, and error analysis.

Studien-/Prüfungsleistungen

Studienleistung: Achieving 50 % of exercise points.

Prüfungsleistung: Completion of the Studienleistung and successful participation in the exam.

Lecture Notes

The lecture notes will be uploaded online regularly during the semester: Version of 21.11.2025

Exercise Groups

Exercise groups begin during the second week of lectures and take place weekly.
TimeRoomTutorSubmission
Monday 16-18 SR 226 (Hermann-Herder-Str. 10) Ilkhom Wednesday, 14:00

Exercise Sheets

The exercise sheets consist partially of exercises from the lecture notes (see below). Submission at the letterbox "Deep Neural Networks" 1 next to CIP-Pool (Hermann-Herder-Str. 10) or via e-mail until Wednesday 14:00. The exercise sheets can be submitted in groups of two. In that case equal contribution from both of the students is expected.

 Sheet Upload date Deadline
Sheet1 14.10.2025 22.10.2025
Exercises 1.2.1, 1.2.3, 1.2.4, 1.2.5, 1.2.6, 1.2.10, and proof of Lemma 1.2.12 21.10.2025 29.10.2025
Sheet3 29.10.2025 05.11.2025
Sheet4 05.11.2025 12.11.2025
Sheet5 12.11.2025 19.11.2025
Sheet6 19.11.2025 26.11.2025