Erwin Riegler: Katalogdaten im Herbstsemester 2019 |
| Name | Herr Dr. Erwin Riegler |
| Adresse | Professur Math. Informationswiss. ETH Zürich, ETF F 107 Sternwartstrasse 7 8092 Zürich SWITZERLAND |
| Telefon | +41 44 633 81 77 |
| eriegler@mins.ee.ethz.ch | |
| Departement | Informationstechnologie und Elektrotechnik |
| Beziehung | Dozent |
| Nummer | Titel | ECTS | Umfang | Dozierende | |
|---|---|---|---|---|---|
| 227-0423-00L | Neural Network Theory | 4 KP | 2V + 1U | H. Bölcskei, E. Riegler | |
| Kurzbeschreibung | The class focuses on fundamental mathematical aspects of neural networks with an emphasis on deep networks: Universal approximation theorems, capacity of separating surfaces, generalization, reproducing Kernel Hilbert spaces, support vector machines, fundamental limits of deep neural network learning, dimension measures, feature extraction with scattering networks | ||||
| Lernziel | After attending this lecture, participating in the exercise sessions, and working on the homework problem sets, students will have acquired a working knowledge of the mathematical foundations of neural networks. | ||||
| Inhalt | 1. Universal approximation with single- and multi-layer networks 2. Geometry of decision surfaces 3. Separating capacity of nonlinear decision surfaces 4. Generalization 5. Reproducing Kernel Hilbert Spaces, support vector machines 6. Deep neural network approximation theory: Fundamental limits on compressibility of signal classes, Kolmogorov epsilon-entropy of signal classes, covering numbers, fundamental limits of deep neural network learning 7. Learning of real-valued functions: Pseudo-dimension, fat-shattering dimension, Vapnik-Chervonenkis dimension 8. Scattering networks | ||||
| Skript | Detailed lecture notes will be provided as we go along. | ||||
| Voraussetzungen / Besonderes | This course is aimed at students with a strong mathematical background in general, and in linear algebra, analysis, and probability theory in particular. | ||||