inf5400 - Advanced Topics in Applied Deep Learning (Complete module description)

Module label	Advanced Topics in Applied Deep Learning
Module code	inf5400
Credit points	6.0 KP
Workload	180 h
Institute directory	Department of Computing Science
Applicability of the module	Master's Programme Business Informatics (Master) > Akzentsetzungsmodule der Informatik Master's Programme Computing Science (Master) > Angewandte Informatik
Responsible persons	Strodthoff, Nils (module responsibility) Lehrenden, Die im Modul (authorised to take exams)
Prerequisites	This module is intended for an advanced audience and requires a solid understanding of the fundamentals of Machine Learning. Experience in training deep neural networks is essential in this context.
Skills to be acquired in this module	Professional competence The students have in-depth knowledge of selected application areas of deep learning. They are familiar with various solutions for problems in these areas, know their advantages and disadvantages, and can practically implement them and adapt them to their own issues. Methodological competence The students independently develop theoretical and practical concepts with the help of in-person events, provided materials, and specialized literature. Social competence The students can present solution approaches for problems in this area to the plenary and defend them in discussions. Self-competence The students are able to assess their own subject-specific and methodological competence. They take responsibility for their competence development and learning progress and reflect on these independently. In addition, they independently work on learning content and can critically reflect on the content.
Module contents	This lecture builds upon the module "Applied Deep Learning in PyTorch" and addresses current research topics at an advanced level of depth. As in the first part, there is a strong emphasis on imparting practical knowledge, which will be learned and reinforced through practical exercises. The thematic areas to be covered in various instantiations of the module include deep learning methods for time series analysis, self-supervised learning methods, and modern generative models.
Recommended reading	Prince, S. J. (2023). Understanding deep learning. MIT press. Dawid, A. & LeCun, Y. (2023). Introduction to Latent Variable Energy-Based Models: A Path Towards Autonomous Machine Intelligence. Les Houches Summer School on Statistical Physics and Machine Learning in 2022 https://arxiv.org/abs/2306.02572
Links
Language of instruction	English
Duration (semesters)	1 Semester
Module frequency	jedes Wintersemester
Module capacity	unlimited
Teaching/Learning method	V+Ü

Type of course	SWS	Frequency	Workload of compulsory attendance
Lecture	2	WiSe	28
Exercises	2	WiSe	28
Total module attendance time			56 h

Examination	Prüfungszeiten	Type of examination
Final exam of module	At the end of the lecture period	Written exam / oral exam / project work