IE 678: Deep Learning (FSS 2026)
Organization
- Lecturer:Prof. Dr.-Ing. Margret Keuper, Simon Forbat
- Type of course: Lecture (in presence), exercises, assignments (6 ECTS points)
- Prerequisites: IE 675b Machine Learning or equivalent
Both the lecture (Tuesday) tutorial (Wednesday) start in the first week. Lecture notes, exercises, assignments, and supplementary material can be found in ILIAS (link should work when you are registered to the course via Portal²).
Content
Machine learning is concerned with building computer systems that improve with experience as well as the study of learning processes, including the design of algorithms that are able to make predictions or extract knowledge from data. Building upon IE 675b Machine Learning, this course focuses on deep learning and introduces basic and advanced deep learning architectures and techniques, training methods and hyperparameter optimization, as well as selected applications.
Tentative topics include:
- Feedforward neural networks
- Backpropagation and parameter optimization
- Machine learning systems
- Training techniques for deep learning models
- Recurrent neural networks
- Convolutional neural networks
- Attention and Transformers
- Deep learning for graphs
- Deep generative modelling
Literature
- I. Goodfellow, Y. Bengio, A. Courville. Deep Learning. The MIT Press, 2017 (http://www.deeplearningbook.org/)
- K.P. Murphy. Probabilistic Machine Learning: An Introduction. The MIT Press, 2022 (https://probml.github.io/pml-book/book1.html)
- K.P. Murphy. Probabilistic Machine Learning: Advanced Topics. The MIT Press, 2023 (https://probml.github.io/pml-book/book2.html)
- I. Drori. The Science of Deep Learning. Cambridge University Press, 2023 (eBook)
- Additional material and articles provided in lecture notes