Inhalt

[ 993MLPEDN2V19 ] VL (*)Deep Learning and Neural Nets II

Versionsauswahl
(*) Leider ist diese Information in Deutsch nicht verfügbar.
Workload Ausbildungslevel Studienfachbereich VerantwortlicheR Semesterstunden Anbietende Uni
3 ECTS M1 - Master 1. Jahr Informatik 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