[ 993MLPEDN2V19 ] VL Deep Learning and Neural Nets II

Workload Education level Study areas Responsible person Hours per week Coordinating university
3 ECTS M1 - Master's programme 1. year (*)Artificial Intelligence G√ľnter Klambauer 2 hpw Johannes Kepler University Linz
Detailed information
Original study plan Master's programme Artificial Intelligence 2021W
Objectives 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.
  • 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
Criteria for evaluation Exam at the end of the semester
Methods Slide presentations, discussions, and code examples
Language English
Changing subject? No
On-site course
Maximum number of participants -
Assignment procedure Direct assignment