250144 VO Neural Network Theory (2019W)

3.00 ECTS (2.00 SWS), SPL 25 - Mathematik

Registration/Deregistration

Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).

Details

Language: English

Examination dates

Lecturers

Philipp Christian Petersen

Classes (iCal) - next class is marked with N

Monday 07.10. 13:15 - 14:45 Seminarraum 7 Oskar-Morgenstern-Platz 1 2.Stock
Monday 14.10. 13:15 - 14:45 Seminarraum 7 Oskar-Morgenstern-Platz 1 2.Stock
Monday 21.10. 13:15 - 14:45 Seminarraum 7 Oskar-Morgenstern-Platz 1 2.Stock
Monday 28.10. 13:15 - 14:45 Seminarraum 7 Oskar-Morgenstern-Platz 1 2.Stock
Monday 04.11. 13:15 - 14:45 Seminarraum 7 Oskar-Morgenstern-Platz 1 2.Stock
Monday 11.11. 13:15 - 14:45 Seminarraum 7 Oskar-Morgenstern-Platz 1 2.Stock
Monday 18.11. 13:15 - 14:45 Seminarraum 7 Oskar-Morgenstern-Platz 1 2.Stock
Monday 25.11. 13:15 - 14:45 Seminarraum 7 Oskar-Morgenstern-Platz 1 2.Stock
Monday 02.12. 13:15 - 14:45 Seminarraum 7 Oskar-Morgenstern-Platz 1 2.Stock
Monday 09.12. 13:15 - 14:45 Seminarraum 7 Oskar-Morgenstern-Platz 1 2.Stock
Monday 16.12. 13:15 - 14:45 Seminarraum 7 Oskar-Morgenstern-Platz 1 2.Stock
Monday 13.01. 13:15 - 14:45 Seminarraum 7 Oskar-Morgenstern-Platz 1 2.Stock
Monday 20.01. 13:15 - 14:45 Seminarraum 7 Oskar-Morgenstern-Platz 1 2.Stock
Monday 27.01. 13:15 - 14:45 Seminarraum 7 Oskar-Morgenstern-Platz 1 2.Stock

Information

Aims, contents and method of the course

Deep neural networks form the backbone of most modern machine learning algorithms. Additionally, neural networks are mathematical objects that can be theoretically analysed to obtain profound insights explaining many phenomena that are observed in applications. In this lecture series, we present a comprehensive collection of such results.

Lecture notes will be developed during the semester.

This class will _not_ discuss algorithms to train deep neural networks for various specific applications.

Assessment and permitted materials

There will be an oral exam at the end of the semester.

Minimum requirements and assessment criteria

The lecture can be followed best with a working knowledge of basic concepts of functional analysis and Fourier analysis.

Examination topics

Everything covered in the course.

Reading list

Peter L. Bartlett, Martin Anthony, Neural Network Learning: Theoretical Foundations, Cambridge University Press,1999
The lecture notes (http://pc-petersen.eu/Neural_Network_Theory.pdf )

Association in the course directory

MAMV; MSTV;

Last modified: Th 11.02.2021 00:25