Advanced Deep Learning - Interactive curriculae of TU Ilmenau

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module properties Advanced Deep Learning in degree program Master Data Science 2026
module number	201314
examination number	220507
department	Department of Computer Science and Automation
ID of group	2252 (Data-intensive Systems and Visualization)
module leader	Prof. Dr. Patrick Mäder
term	summer term only
language	Englisch
credit points	5
on-campus program (h)	45
self-study (h)	105
obligation	obligatory module
exam	examination performance with multiple performances
details of the certificate	Das Modul Advanced Deep Learning mit der Prüfungsnummer 220507 schließt mit folgenden Leistungen ab: alternative semesterbegleitende Prüfungsleistung mit einer Wichtung von 50% (Prüfungsnummer: 2200902) alternative semesterbegleitende Prüfungsleistung mit einer Wichtung von 50% (Prüfungsnummer: 2200903) Details zum Abschluss Teilleistung 1: one or multiple written tests consisting of multiple-choice and free-form questions evaluating the professional competence in the course's topics preferably conducted digitally via Moodle and on the students' personal devices final results may be scaled or individual questions may be excluded depending on statistical analysis of the results Details zum Abschluss Teilleistung 2: examination via practical, e.g. coding, assignments to be conducted in person in class or at home evaluating methodological and practical competence potentially presenting the results in class
link to Moodle course
teacher	Prof. Mäder
signup details for alternative examinations	This module contains at least one alternative exam part. Please note that this must usually be registered at the beginning of the semester in which it is offered. The lecturer and/or the examination office will inform you about the details and time periods. If necessary, be sure to ask the lecturer.
maximum number of participants
previous knowledge and experience	. Theoretical foundations of deep neural networks . Programming knowledge (Python) . Deep Learning frameworks (TensorFlow or PyTorch) . Fundamentals of mathematics (linear algebra, analysis)
learning outcome	Professional competence gained through lectures and examined through written exams: Students have gainedacquired and can demonstrate the knowledge into . - understand advanced concepts of deep learning, with a focus on Transformers and Recurrent Neural Networks (RNNs) - analyzinge and implementing models for unsupervised and self-supervised learning such as Generative Adversarial Networks (GANs), autoencoders, and diffusion models - critically comparinge different deep learning architectures and evaluate their strengths and weaknesses for specific problems - be able to applying deep learning models to real-world applications in various fields such as computer vision, natural language processing, graph analysis Methodological competence gained through seminars and examined through practical evaluation: Students gained acquired the ability . - to implement and apply a variety of advanced deep learning algorithms with TensorFlow or PyTorch - to evaluate, optimize and troubleshoot advanced deep learning models - to use computational resources for training and application of advanced deep learning models Social competence gained through lectures and seminars: - Students gained insights in ethical aspects of machine learning (e.g., bias, autonomous driving) through discussions in lectures and seminars. - Students can discuss advantages and disadvantages of different deep learning approaches among each other and with their lecturers and gained professionality in mastering discussions beyond their mother tongue.
content	The topics covered include: . Deep neural networks for processing sequential data. Review of Convolutional Neural Networks (CNNs), introduction to Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRUs) and their applications in speech (NLP) and image processing. . Representations of natural language in isolated, embedded and contextual form. . Transformer architectures for NLP and image processing . Generative models: Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs) and Diffusion Models. Understanding the mathematical foundations and implementation of these models to generate new data. . Introduction of graph representations and corresponding architectures . Distributed and federated learning, aspects of data security
media of instruction and technical requirements for education and examination in case of online participation	Beamer Presentations, Homework task sets as PDF, Assignments including code stubs, Jupyter notebooks
literature / references	"Deep Learning" von Ian Goodfellow, Yoshua Bengio und Aaron Courville "Machine Learning with PyTorch and Scikit-Learn" von S. Raschka "Generative Deep Learning" von David Foster
evaluation of teaching