Universität Wien

250074 VO Deep Learning (2017S)

5.00 ECTS (3.00 SWS), SPL 25 - Mathematik

Details

Language: English

Examination dates

Lecturers

Classes (iCal) - next class is marked with N

  • Wednesday 01.03. 13:15 - 14:00 Seminarraum 12 Oskar-Morgenstern-Platz 1 2.Stock
  • Monday 06.03. 11:30 - 13:00 Hörsaal 8 Oskar-Morgenstern-Platz 1 1.Stock
  • Wednesday 08.03. 13:15 - 14:00 Hörsaal 6 Oskar-Morgenstern-Platz 1 1.Stock
  • Wednesday 15.03. 13:15 - 14:45 Hörsaal 10 Oskar-Morgenstern-Platz 1 2.Stock
  • Monday 20.03. 11:30 - 13:00 Hörsaal 4 Oskar-Morgenstern-Platz 1 Erdgeschoß
  • Wednesday 22.03. 13:15 - 14:00 Hörsaal 14 Oskar-Morgenstern-Platz 1 2.Stock
  • Monday 27.03. 11:30 - 13:00 Hörsaal 4 Oskar-Morgenstern-Platz 1 Erdgeschoß
  • Wednesday 29.03. 13:15 - 14:45 Hörsaal 4 Oskar-Morgenstern-Platz 1 Erdgeschoß
  • Monday 03.04. 11:30 - 13:00 Seminarraum 12 Oskar-Morgenstern-Platz 1 2.Stock
  • Wednesday 05.04. 13:15 - 14:45 Hörsaal 4 Oskar-Morgenstern-Platz 1 Erdgeschoß
  • Monday 24.04. 11:30 - 13:00 Hörsaal 4 Oskar-Morgenstern-Platz 1 Erdgeschoß
  • Wednesday 26.04. 13:15 - 14:00 Hörsaal 6 Oskar-Morgenstern-Platz 1 1.Stock
  • Wednesday 03.05. 13:15 - 14:00 Hörsaal 6 Oskar-Morgenstern-Platz 1 1.Stock
  • Monday 08.05. 11:30 - 13:00 Hörsaal 14 Oskar-Morgenstern-Platz 1 2.Stock
  • Wednesday 10.05. 13:15 - 14:00 Hörsaal 6 Oskar-Morgenstern-Platz 1 1.Stock
  • Monday 15.05. 11:30 - 13:00 Hörsaal 4 Oskar-Morgenstern-Platz 1 Erdgeschoß
  • Wednesday 17.05. 13:15 - 14:45 Hörsaal 4 Oskar-Morgenstern-Platz 1 Erdgeschoß
  • Monday 22.05. 11:30 - 13:00 Hörsaal 4 Oskar-Morgenstern-Platz 1 Erdgeschoß
  • Wednesday 24.05. 13:15 - 14:45 Hörsaal 4 Oskar-Morgenstern-Platz 1 Erdgeschoß
  • Monday 29.05. 11:30 - 13:00 Seminarraum 12 Oskar-Morgenstern-Platz 1 2.Stock
  • Wednesday 31.05. 13:15 - 14:00 Seminarraum 12 Oskar-Morgenstern-Platz 1 2.Stock
  • Wednesday 07.06. 13:15 - 14:00 Hörsaal 10 Oskar-Morgenstern-Platz 1 2.Stock
  • Monday 12.06. 11:30 - 13:00 Hörsaal 8 Oskar-Morgenstern-Platz 1 1.Stock
  • Wednesday 14.06. 13:15 - 14:00 Hörsaal 7 Oskar-Morgenstern-Platz 1 1.Stock
  • Monday 19.06. 11:30 - 13:00 Hörsaal 8 Oskar-Morgenstern-Platz 1 1.Stock
  • Wednesday 21.06. 13:15 - 14:00 Hörsaal 9 Oskar-Morgenstern-Platz 1 1.Stock
  • Monday 26.06. 11:30 - 13:00 Hörsaal 8 Oskar-Morgenstern-Platz 1 1.Stock
  • Wednesday 28.06. 13:15 - 14:00 Hörsaal 9 Oskar-Morgenstern-Platz 1 1.Stock

Information

Aims, contents and method of the course

The field of deep learning has made big headlines in the past years and is currently revolutionizing artificial intelligence. In this course we aim to obtain an overview of recent developents.

After a motivation we will cover basic statistical learning theory. After that we will study neural networks and convolutional neural networks, together with associated learning algorithms.

Algorithmic aspects will also be covered.

After taking this couse, the students will be able to implement their own neural-network-based AI engine.

More information can be found at http://mat.univie.ac.at/~grohs/DeepLearningCourse.

Assessment and permitted materials

Oral exam

Minimum requirements and assessment criteria

Mandatory: Linear Algebra, Analysis
Highly Desirable: Probability Theory, Functional Analysis
Desirable: Optimization, Numerical Analysis

Examination topics

Reading list

I. Goodfellow, Y. Bengio and A. Courville. Deep Learning. MIT Press (2016), available from http://www.deeplearningbook.org/.

F. Cucker and S. Smale. On the Mathematical Foundations of Learning. Bulletin of the AMS 39/1, pp. 1 -- 49 (2001).

M. Nielsen. Neural Networks and Deep Learning. Available from http://neuralnetworksanddeeplearning.com/

Association in the course directory

MAMV

Last modified: Mo 07.09.2020 15:40