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250106 VO Neural Network Theory (2021W)
Labels
MIXED
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
Monday
07.02.2022
Tuesday
08.02.2022
Wednesday
09.02.2022
Thursday
10.02.2022
Friday
11.02.2022
Monday
28.02.2022
Tuesday
01.03.2022
Thursday
03.03.2022
Friday
04.03.2022
Monday
07.03.2022
Tuesday
08.03.2022
Wednesday
27.04.2022
Monday
18.07.2022
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,1999The 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