phy731 Compulsory Optional Subject Theory (Course overview)

Institute of Physics

6 KP

Module components

Semester courses Wintersemester 2026/2027

Examination

Lecture

5.04.4204 - Prinzipien der Signalverarbeitung in Hörgeräten
- Dr. rer. nat. Giso Grimm
- Dr. rer. nat. Hendrik Kayser
- Thursday, 10:00 - 12:00, Weekly (from 15.10.26)
Understanding the signal processing principles applied to hearing devices (hearing aids and cochlear implants) Contents: - Amplification and compression - Speech enhancement and noise reduction - Signal processing in cochlear implants - Computational auditory scene analysis - Automatic classification of the acoustic environment - Acoustic feedback management
5.04.4207 - Processing and analysis of biomedical data
- Prof. Dr. Stefan Uppenkamp, Dipl.-Phys.
- Dr. Stephan Ewert
- Thomas Brand
- Monday, 08:00 - 10:00, Weekly (from 12.10.26)
- Thursday, 08:00 - 10:00, Weekly (from 15.10.26)
This course introduces basic concepts of statistics and signal processing and applies them to real-world examples of bio-medical data. In the second part of the course, recorded datasets are noise-reduced, analyzed, and discussed in views of which statistical tests and analysis methods are appropriate for the underlying data. The course forms a bridge between theory and application and offers the students the means and tools to set up and analyze their future datasets in a meaningful manner. content: Normal distributions and significance testing, Monte-Carlo bootstrap techniques, Linear regression, Correlation, Signal-to-noise estimation, Principal component analysis, Confi-dence intervals, Dipole source analysis, Analysis of variance Each technique is explained, tested and discussed in the exercises.
6.07.5400 - Deep Unsupervised Learning
- M A Al-Masud
- Dr. rer. nat. Juan Lopez Alcaraz
- Prof. Dr. Nils Strodthoff
- Monday, 12:00 - 14:00, Weekly (from 12.10.26)
- Thursday, 08:00 - 10:00, Weekly (from 15.10.26)
This lecture encompasses two primary subjects: self-supervised learning and modern generative models. In the first part, we will examine the fundamental design principles (contrastive versus non-contrastive) underlying self-supervised learning algorithms. In the second part, we will explore applications of these principles to specific data modalities such as computer vision, natural language processing (including an extensive coverage of large language models) and audio/time series. Finally, the third part will focus on generative models, where we will cover a wide array of models, ranging from autoregressive models, variational autoencoders, and normalizing flows, to generative adversarial networks and (latent) diffusion models.
6.07.5408_L - Applied Deep Learning
- Maham Khokhar
- Dr. rer. nat. Juan Lopez Alcaraz
- Zahra Mansour
- Nils Neukirch
- Prof. Dr. Nils Strodthoff
- Friday, 12:00 - 14:00, Weekly (from 16.10.26)
This lecture provides a comprehensive introduction to contemporary Deep Learning methods, with a specific emphasis on their practical application. Concurrently, it serves as a primer for the widely-used PyTorch Deep Learning framework, assuming only a basic familiarity with Python. The course encompasses a wide range of prevalent machine learning tasks across various data types, including tabular, image, text, audio, and graph data. Throughout the course, we delve into the most crucial and up-to-date model architectures within these domains. This encompasses convolutional neural networks, recurrent neural networks, and transformer models. The lecture is complemented by hands-on exercise sessions, where students will gain practical proficiency with PyTorch. Simultaneously, they will acquire practical insights to effectively apply contemporary deep learning methods within their specific fields of interest.

Seminar

Exercises

6.07.5408_E1 - Applied Deep Learning
- Dr. rer. nat. Juan Lopez Alcaraz
- Zahra Mansour
- Prof. Dr. Nils Strodthoff
- Wednesday, 16:00 - 18:00, Weekly (from 14.10.26)
6.07.5408_E2 - Applied Deep Learning
- Maham Khokhar
- Dr. rer. nat. Juan Lopez Alcaraz
- Prof. Dr. Nils Strodthoff
- Monday, 12:00 - 14:00, Weekly (from 12.10.26)
6.07.5408_E3 - Applied Deep Learning
- Dr. rer. nat. Juan Lopez Alcaraz
- Nils Neukirch
- Prof. Dr. Nils Strodthoff
- Wednesday, 12:00 - 14:00, Weekly (from 14.10.26)
5.04.4207 - Processing and analysis of biomedical data
- Prof. Dr. Stefan Uppenkamp, Dipl.-Phys.
- Dr. Stephan Ewert
- Thomas Brand
- Monday, 08:00 - 10:00, Weekly (from 12.10.26)
- Thursday, 08:00 - 10:00, Weekly (from 15.10.26)
This course introduces basic concepts of statistics and signal processing and applies them to real-world examples of bio-medical data. In the second part of the course, recorded datasets are noise-reduced, analyzed, and discussed in views of which statistical tests and analysis methods are appropriate for the underlying data. The course forms a bridge between theory and application and offers the students the means and tools to set up and analyze their future datasets in a meaningful manner. content: Normal distributions and significance testing, Monte-Carlo bootstrap techniques, Linear regression, Correlation, Signal-to-noise estimation, Principal component analysis, Confi-dence intervals, Dipole source analysis, Analysis of variance Each technique is explained, tested and discussed in the exercises.
6.07.5400 - Deep Unsupervised Learning
- M A Al-Masud
- Dr. rer. nat. Juan Lopez Alcaraz
- Prof. Dr. Nils Strodthoff
- Monday, 12:00 - 14:00, Weekly (from 12.10.26)
- Thursday, 08:00 - 10:00, Weekly (from 15.10.26)
This lecture encompasses two primary subjects: self-supervised learning and modern generative models. In the first part, we will examine the fundamental design principles (contrastive versus non-contrastive) underlying self-supervised learning algorithms. In the second part, we will explore applications of these principles to specific data modalities such as computer vision, natural language processing (including an extensive coverage of large language models) and audio/time series. Finally, the third part will focus on generative models, where we will cover a wide array of models, ranging from autoregressive models, variational autoencoders, and normalizing flows, to generative adversarial networks and (latent) diffusion models.

Hinweise zum Modul

Prerequisites	Bachelor in Physik, Technik und Medizin oder entsprechender Abschluss
Further information	Module Capacity: unrestricted
Method of assesment	M
Learning outcomes/competencies	Die Studierenden erwerben die theoretischen Voraussetzungen für die numerische und analytische Modellierung komplexer Vorgänge in der Medizin, Biologie und Biophysik, und wenden Forschungsmethoden des Exzellenzcluster Hearing4all im Modellierungsbereich an. Spezielle Kompetenzen abhängig von der gewählten Veranstaltung.