[ 993MLPEDN2V19 ] VL (*)Deep Learning and Neural Nets II
|
|
|
|
(*) Leider ist diese Information in Deutsch nicht verfügbar. |
|
Workload |
Ausbildungslevel |
Studienfachbereich |
VerantwortlicheR |
Semesterstunden |
Anbietende Uni |
3 ECTS |
M1 - Master 1. Jahr |
Artificial Intelligence |
Günter Klambauer |
2 SSt |
Johannes Kepler Universität Linz |
|
|
|
Detailinformationen |
Quellcurriculum |
Masterstudium Artificial Intelligence 2021W |
Ziele |
(*)Deep learning is a machine learning technique based on artificial neural networks. In this lecture, students will see more advanced insights, extensions and applications of deep learning, as well as discuss unsupervised deep learning techniques and open research questions. It is expected that students visiting this class already have a solid understanding of machine learning.
|
Lehrinhalte |
(*)- Convolutional neural networks for classification, segmentation, and object detection
- Generative and unsupervised Deep Learning
- Variational autoencoders
- Generative adversarial networks
- Bayesian Deep Learning
- Energy-based models
- Deep Learning theory
|
Beurteilungskriterien |
(*)Exam at the end of the semester
|
Lehrmethoden |
(*)Slide presentations, discussions, and code examples
|
Abhaltungssprache |
Englisch |
Lehrinhalte wechselnd? |
Nein |
|
|
|
Präsenzlehrveranstaltung |
Teilungsziffer |
- |
Zuteilungsverfahren |
Direktzuteilung |
|