Universität Wien
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250106 VO Neural Network Theory (2021W)

3.00 ECTS (2.00 SWS), SPL 25 - Mathematik
MIXED
Moodle

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Details

Language: English

Examination dates

Lecturers

Classes (iCal) - next class is marked with N

  • Monday 04.10. 16:45 - 18:15 Seminarraum 10 Oskar-Morgenstern-Platz 1 2.Stock
  • Monday 11.10. 16:45 - 18:15 Seminarraum 10 Oskar-Morgenstern-Platz 1 2.Stock
  • Monday 18.10. 16:45 - 18:15 Seminarraum 10 Oskar-Morgenstern-Platz 1 2.Stock
  • Monday 25.10. 16:45 - 18:15 Seminarraum 10 Oskar-Morgenstern-Platz 1 2.Stock
  • Monday 08.11. 16:45 - 18:15 Seminarraum 10 Oskar-Morgenstern-Platz 1 2.Stock
  • Monday 15.11. 16:45 - 18:15 Seminarraum 10 Oskar-Morgenstern-Platz 1 2.Stock
  • Monday 22.11. 16:45 - 18:15 Seminarraum 10 Oskar-Morgenstern-Platz 1 2.Stock
  • Monday 29.11. 16:45 - 18:15 Seminarraum 10 Oskar-Morgenstern-Platz 1 2.Stock
  • Monday 06.12. 16:45 - 18:15 Seminarraum 10 Oskar-Morgenstern-Platz 1 2.Stock
  • Monday 13.12. 16:45 - 18:15 Seminarraum 10 Oskar-Morgenstern-Platz 1 2.Stock
  • Monday 10.01. 16:45 - 18:15 Seminarraum 10 Oskar-Morgenstern-Platz 1 2.Stock
  • Monday 17.01. 16:45 - 18:15 Seminarraum 10 Oskar-Morgenstern-Platz 1 2.Stock
  • Monday 24.01. 16:45 - 18:15 Seminarraum 10 Oskar-Morgenstern-Platz 1 2.Stock
  • Monday 31.01. 16:45 - 18:15 Seminarraum 10 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 supplied.

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

Assessment and permitted materials

There will be a written or 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: Mo 25.07.2022 13:28