Lehrveranstaltungsverzeichnis Masterstudiengang Artificial Intelligence (AI)

Seminar Visual Computing [VCHS(AR)] Dozentinnen/Dozenten: Tobias Günther, Tim Weyrich Angaben: Masterseminar, 2 SWS, benoteter Schein, ECTS: 5, nur Fachstudium Termine: Mi, 16:15 - 17:45, 01.151-128 Studienrichtungen / Studienfächer: WPF AI-MA ab 1 Voraussetzungen / Organisatorisches: In this seminar, attendance is mandatory. Up to three missed sessions are allowed. In case of absence due to medical or other exceptional reasons, exceptions can be made if the lecturers are informed before the missed seminar session. Inhalt: This seminar covers advanced topics in visual computing, including both seminal research papers, as well as the latest research results. The seminar provides an opportunity to obtain a comprehensive overview of research questions in visual computing, as well as allows students to dive deeper into a chosen topic. Each student presents one scientific publication and explains its content to fellow students taking the course. Thereby, students practice their argumentation and presentation skills. For each paper, a supervisor is provided, who answers questions and gives pointers on the presentation slide design. The seminar is concluded with a short written report. The main topics include: human performance capture (faces, eyes, speech), animation (motion controllers, speech synthesis, shape modelling) fabrication (caustic design, robot design), appearance modelling (subsurface scattering), Monte Carlo rendering (importance sampling, participating media), differentiable rendering (neural rendering, inverse rendering), denoising (non-local means and deep learning), physics simulation (fluid simulation) (automatisch geplant, erwartete Hörerzahl original: 10, fixe Veranstaltung: nein) Schlagwörter: Visual Computing

AI-1 Systems Project [AI1SysProj] Dozentinnen/Dozenten: Michael Kohlhase, Jan Frederik Schäfer Angaben: Projektseminar, 4 SWS, ECTS: 10 Termine: Di, 14:15 - 16:00, Zoom-Meeting Studienrichtungen / Studienfächer: WPF AI-MA ab 1

Master-Projekt Datenmanagement Dozentinnen/Dozenten: Richard Lenz, Viktor Leis, Alle Assistenten Angaben: Projektseminar, 2 SWS, ECTS: 10, nur Fachstudium Termine: unregelmäßig, nach Bedarf Studienrichtungen / Studienfächer: WPF AI-MA 3 Inhalt: Da wir in der Vergangenheit immer mit sehr kleinen Teilnehmerzahlen in unseren Master-Projekten zu tun hatten, haben wir sie nun (nach dem Vorbild anderer Lehrstühle) anders organisiert: Die Teilnehmer erhalten individuelle Aufgaben, die sich in den Projekten am Lehrstuhl ergeben haben und die sich im Rahmen einer solchen Lehrveranstaltung lösen lassen. Sowohl die Wissenschaftlichkeit als auch die erwünschte Team-Arbeit sind durch die Einbettung in diese Projekte gegeben, selbst bei nur einem Teilnehmer oder einer Teilnehmerin. Im Unterschied zu den Examensarbeiten wird die praktische Arbeit einen viel größeren Anteil einnehmen. Literaturarbeit und Dokumentation der Ergebnisse sind immer noch erwünscht, fallen aber deutlich geringer aus als bei Examensarbeiten. In erster Linie wird an Forschungsprototypen mitgearbeitet, die in den Projekten am Lehrstuhl erstellt werden. Das kann Codierung bedeuten, aber auch Messungen und Simulationen, um nur einige Beispiele zu nennen. Wir schlagen Themen vor, aber es ist durchaus zulässig, sich auch selbst Gedanken über ein Thema zu machen. Naheliegende Voraussetzung dafür ist es, sich mit den Projekten am Lehrstuhl zu befassen (siehe Orientierungsvorlesung!) und auch mit den Mitarbeitern zu sprechen, die diese Projekte durchführen. Themenvorschläge finden sich im zugeordneten StudOn-Kurs: https://www.studon.fau.de/crs1948594.html (erwartete Hörerzahl original: 10, fixe Veranstaltung: nein) Schlagwörter: Master; Projekt; Project; Masterprojekt; Master Projekt; EDEN; BATS; TDQMed; DSAM

Fantastic datasets and where to find them [FANDAT] Dozentinnen/Dozenten: Andreas Kist, René Groh Angaben: Seminar, 2 SWS, benoteter Schein, ECTS: 2,5, nur Fachstudium, für FAU Scientia Gaststudierende zugelassen Termine: Do, 13:15 - 14:45, Zoom-Meeting Online only seminar, please register via StudOn to have access to the Zoom link Studienrichtungen / Studienfächer: WPF AI-MA ab 1 Inhalt: Im ersten Zoom-Meeting werden verschiedene biomedizinische Datensätze aufgezeigt, wo man diese finden kann und als Vortrag vergeben. Ziel des Seminars ist es, dass Studenten durch das Vorstellen eines biomedizinischen Datensatzes die Grundlagen der Datenakquise, den Umfang des Datensatzes, sowie die Vor- und Nachteile eines Datensatzes verstehen.

	Green AI - AI for Sustainability and Sustainability of AI [GREENAI] Angaben: Seminar, 2 SWS, benoteter Schein, ECTS: 5, für FAU Scientia Gaststudierende zugelassen, There are no more free places in the SS 2022. Studienrichtungen / Studienfächer: WPF AI-MA ab 1
		Mi	10:15 - 11:45	00.010		Dorschky, E. Raab, R. Eskofier, B.

Maschinelles Lernen und Datenanalytik für Industrie 4.0 Dozentinnen/Dozenten: Björn Eskofier, Johannes Roider, Christoph Scholl, Lukas Schmidt Angaben: Seminar, 2 SWS, benoteter Schein, ECTS: 5, nur Fachstudium, für FAU Scientia Gaststudierende zugelassen, Registration via mail to johannes.roider@fau.de Termine: Mi, 16:15 - 18:00, 00.010 Starts April 27th 2022 Studienrichtungen / Studienfächer: WPF AI-MA ab 1 Voraussetzungen / Organisatorisches: Registration via e-mail to johannes.roider@fau.de Registration period: 25.02.-04.05.2022 The seminar will be held face-to-face. Requirements: Prior knowledge of machine learning via courses like PA, IntroPR, PR, DL, MLTS, CVP or equivalent (ideally first project experiences) is expected! Motivation to explore scientific findings (e.g. via literature research) Motivation to code and analyze data Please state your previous experience in machine learning (e. g. Which courses did you take? Which project experience do you have?) when registering for the course. Examination: 50% of grade: Presentation + demo (20 minutes) 50% of grade 4 pages IEEE standard paper (excluding references) (+ code submission) Attendance of all meetings is required. Inhalt: Contents Companies in all kinds of industries are producing and collecting rapidly more and more data from various sources. This is enabled by technologies such as the Internet of Things (IoT), Cyber-physical system (CPS) and cloud computing. Hence there is an increasing demand in industry and research for students and graduates with machine learning and data analytics skills in the Industry 4.0 context. In this Seminar the Industry 4.0 term will include adjacent fields like the medical device or the automotive sector. Aim of this seminar is to give students insights about state-of-the-art machine learning and data analytics methods and applications in Industry 4.0 and adjacent fields. Students will mainly work independently on either a implementation centric or a research centric topic. The implementation centric topics will focus primarily on the implementation of algorithms and analytical components, while the research centric topic will focus on researching and structuring literature on a specific field of interest. Several potential topics will be provided but students are also encouraged to propose their own topics (please discuss with teaching staff beforehand). Topics covered will include but are not limited to: Best practices for presentation and scientific work Brief overview of current hot topics in the field of machine learning and data analytics for Industry 4.0 (e.g. deep learning for predictive maintenance and process mining for usage analysis) Data acquisition (what kind of data can be acquired? Identification of publicly available data sets) and storage (how can data be stored efficiently?) Machine learning and data analytics methodologies (Support vector machines, Hidden Markov models, Deep learning, Process mining, etc.) for industrial data (sensor data, event logs, ...) The seminar will include talks by corresponding lecturer and invited experts in the domain. Furthermore, students will present results from literature research and data analytics projects. Learning Objectives and Competencies Students will develop an understanding of the current hot field of machine learning and data analytics for Industry 4.0 / healthcare / automotive Students will learn to research and present a topic within the context of machine learning and data analytics for Industry 4.0 / healthcare / automotive independently Students will learn to identify opportunities, challenges and limitations of corresponding ML approaches for Industry 4.0 / healthcare / automotive Students will develop the skill to identify and understand relevant literature and to present their finding in a structured manner Students will learn to present implementation and validation results in form of a demonstration and/or report (erwartete Hörerzahl original: 7, fixe Veranstaltung: nein) Empfohlene Literatur: Lei, Yaguo, Naipeng Li, Liang Guo, Ningbo Li, Tao Yan, and Jing Lin. "Machinery Health Prognostics: A Systematic Review from Data Acquisition to RUL Prediction.” Mechanical Systems and Signal Processing 104 (May 2018): 799–834.https://doi.org/10.1016/j.ymssp.2017.11.016. Rojas, Eric, Jorge Munoz-Gama, Marcos Sepúlveda, and Daniel Capurro. "Process Mining in Healthcare: A Literature Review.” Journal of Biomedical Informatics 61 (June 1, 2016): 224–36. https://doi.org/10.1016/j.jbi.2016.04.007. Wil M. P. van der Aalst. „Process Mining: Data Science in Action” 2nd edition, Springer 2016. ISBN 978-3-662-49851-4 Wang, Lihui, and Xi Vincent Wang. Cloud-Based Cyber-Physical Systems in Manufacturing. Cham: Springer International Publishing, 2018. https://doi.org/10.1007/978-3-319-67693-7. Schlagwörter: Machine Learning, Data Analytics, Process Mining, Predictive Maintenance, Industry 4.0, Healthcare, Automotive

Seminar Humans in the Loop: The Design of Interactive AI Systems Dozent/in: Bernhard Kainz Angaben: Seminar, 2 SWS, ECTS: 5 Termine: Zeit/Ort n.V. Studienrichtungen / Studienfächer: WPF AI-MA ab 2 Voraussetzungen / Organisatorisches: recommended prerequisites: Deep Learning ML Prof. Dr. Andreas Maier 2+2 5 x E Pattern Recognition ML Prof. Dr. Andreas Maier 3+1+2 5 x E Maschinelles Lernen für Zeitreihen ML Prof. Eskofier, Prof. Oliver Amft, Dr. Ch. Mutschler 2+2+2 7.5 x E Inhalt: Human-in-the-Loop Machine Learning describes processes in which humans and Machine Learning algorithms interact to solve one or more of the following: Making Machine Learning more accurate Getting Machine Learning to the desired accuracy faster Making humans more accurate Making humans more efficient Aim of this seminar is to give students insights about state-of-the-art Active Learning and interactive data analysis methods. Students will work independently on specific topics including implementation and analytical components alongside lectures delivered by the course lead, guest lectures and flipped classroom sessions, where students explore a topic independently, which is then discussed in class. Several potential topics will be provided but students are also encouraged to propose their own topics (after discussion with course lead). Topics covered will include but are not limited to: Introduction to Human-in-the-Loop Machine Learning Active Learning Strategies: Uncertainty Sampling Diversity Sampling Other Strategies Annotating Data for Machine Learning Who are the right people to annotate your data? Quality control for data annotation User interfaces for data annotation Transfer Learning and Pre-Trained Models What are Embeddings? What is Transfer Learning? Adaptive Learning Machine-Learning for aiding human annotation Advanced Human-in-the-Loop Machine Learning (erwartete Hörerzahl original: 10, fixe Veranstaltung: nein) Empfohlene Literatur: 17 Bibliography A specific reading list will be established at the beginning of each term, general literature is listed below: Quinn J, McEachen J, Fullan M, Gardner M, Drummy M. Dive into deep learning: Tools for engagement. Corwin Press; 2019 Jul 15. https://d2l.ai/ Goodfellow I, Bengio Y, Courville A, Bengio Y. Deep learning. Cambridge: MIT press; 2016 Nov 18. https://www.deeplearningbook.org/ Budd S, Robinson EC, Kainz B. A survey on active learning and human-in-the-loop deep learning for medical image analysis. arXiv preprint arXiv:1910.02923. 2019 Oct 7. https://arxiv.org/abs/1910.02923

Computational Imaging Project Dozentinnen/Dozenten: Florian Knoll, Zhengguo Tan Angaben: Sonstige Lehrveranstaltung, 8 SWS, ECTS: 10, nur Fachstudium, für FAU Scientia Gaststudierende zugelassen Termine: Zeit/Ort n.V. Studienrichtungen / Studienfächer: WPF AI-MA 1

Project Representation Learning Dozent/in: Bernhard Kainz Angaben: Sonstige Lehrveranstaltung, 8 SWS, ECTS: 10, nur Fachstudium Termine: Zeit/Ort n.V. Studienrichtungen / Studienfächer: WPF AI-MA ab 1 Voraussetzungen / Organisatorisches: recommended: Deep Learning ML Prof. Dr. Andreas Maier 2+2 5 x E Pattern Recognition ML Prof. Dr. Andreas Maier 3+1+2 5 x E Maschinelles Lernen für Zeitreihen ML Prof. Eskofier, Prof. Oliver Amft, Dr. Ch. Mutschler 2+2+2 7.5 x E Inhalt: Different projects in the area of (deep) representation learning are on offer. These reach from theoretical exploration of new data representation methods to practical evaluation of applications in, e.g., medical image analysis. Example projects will be made available on the website of the IDEA Lab https://idea.tf.fau.eu/. Students may also propose their own projects, which will be coordinated and refined with the module lead during preliminary discussions. (erwartete Hörerzahl original: 10, fixe Veranstaltung: nein) Empfohlene Literatur: A specific reading list will be established at the beginning of each project, general literature is listed below: Quinn J, McEachen J, Fullan M, Gardner M, Drummy M. Dive into deep learning: Tools for engagement. Corwin Press; 2019 Jul 15. https://d2l.ai/ Goodfellow I, Bengio Y, Courville A, Bengio Y. Deep learning. Cambridge: MIT press; 2016 Nov 18. https://www.deeplearningbook.org/

Projekt Mustererkennung Dozent/in: Andreas Maier Angaben: Sonstige Lehrveranstaltung, benoteter Schein, ECTS: 10, At the Pattern Recognition Lab we offer project topics that are connected to our current research in the fields of medical image processing, speech processing and understanding, computer vision and digital sports. Other than a course with fixed topic, project topics are defined individually. The 10 ECTS project is directed towards students of computer science. However, most projects can also be offered as 5 ECTS medical engineering Academic Lab or Research Lab. Please have a look at our website for an overview: https://lme.tf.fau.de/teaching/thesis/ Termine: Zeit/Ort n.V. Studienrichtungen / Studienfächer: WPF AI-MA ab 1 Inhalt: Es werden mehrere verschiedene Aufgabenstellungen angeboten. Details zum Thema und der Bearbeitungszeit finden sich unter http://www5.cs.fau/theses/masterproject (erwartete Hörerzahl original: 12, fixe Veranstaltung: nein) Schlagwörter: Master Projekt Project

	The AMOS Project (UE) [OSS-AMOS-UE(A)] Dozent/in: Dirk Riehle Angaben: Übung, für FAU Scientia Gaststudierende zugelassen Studienrichtungen / Studienfächer: WPF AI-MA ab 1 Inhalt: This course teaches agile methods (Scrum and XP) and open source tools using a single semester-long project. Topics covered are: Agile methods and related software development processes Scrum roles, process practices, including product and engineering management Technical practices like refactoring, continuous integration, and test-driven development Principles and best practices of open source software development The project is a software development project in which each student team works with an industry partner who provides the idea for the project. This is a practical hands-on experience. Students can play one of two primary roles: Product owner. In this function, a student defines, prioritizes, communicates, and reviews requirements. The total effort adds up to 5 ECTS. Software developer. In this function, a student estimates their effort for requirements and implements them. The total effort adds up to 10 ECTS. Students will be organized into teams of 7-8 people, combining product owners with software developers. An industry partner will provide requirements to be worked out in detail by the product owners and to be realized by the software developers. The available projects will be presented in the run-up to the course. Class consists of a 90min lecture followed by a 90min team meeting. Rooms and times for team meetings are assigned at the beginning of the semester. You must be able to regularly participate in the team meetings. If you can't, do not sign up for this course. Students choosing the software developer role must have prior software development experience. Sign-up and further course information are available at https://amos.uni1.de - please sign up for the course on StudOn (available through previous link) as soon as possible. The course information will also tell you how the course will be held (online or in person). (automatisch geplant, erwartete Hörerzahl original: 10, fixe Veranstaltung: nein) (automatisch geplant, erwartete Hörerzahl original: 5, fixe Veranstaltung: nein) (automatisch geplant, erwartete Hörerzahl original: 10, fixe Veranstaltung: nein)
		Mo	08:15 - 09:45	Übung 3 / 01.252-128		Riehle, D.
		Mo	18:15 - 19:45	00.152-113		Riehle, D.
		Di	18:15 - 19:45	01.255-128		Riehle, D.
		Mi	08:15 - 09:45	Übung 3 / 01.252-128		Riehle, D.
		Mi	14:15 - 15:45	01.151-128		Riehle, D.
		Do	08:15 - 09:45	Übung 3 / 01.252-128		Riehle, D.
		Do	10:15 - 11:45	Übung 3 / 01.252-128		Riehle, D.
		Fr	16:15 - 17:45	Übung 3 / 01.252-128		Riehle, D.

	Übungen zu Wissensrepräsentation und -verarbeitung [ÜWuV(A)] Dozent/in: Florian Rabe Angaben: Übung, 2 SWS, Won't take place in the first week. Takes place in the same zoom room as the lecture. Studienrichtungen / Studienfächer: WPF AI-MA ab 1
		Do	14:15 - 15:45	01.255-128		Rabe, F.

Biomedizinische Signalanalyse [BioSig(A)] Dozent/in: Björn Eskofier Angaben: Vorlesung, 2 SWS, ECTS: 2,5 Termine: Mi, 08:15 - 09:45, H10 Online Studienrichtungen / Studienfächer: WPF AI-MA ab 1 Inhalt: Im Rahmen der Vorlesung werden (a) die Grundlagen der Generation von wichtigen Biosignalen im menschlichen Körper (b) die Messung von Biosignalen und (c) Methoden zur Analyse von Biosignalen erläutert und dargestellt. Aufgrund der derzeitigen Corona-Lage findet die Vorlesung digital statt. Für weitere Informationen, wie man sich in die digitalen Räume einloggen kann, besuchen Sie bitte unseren zugehörigen StudOn Kurs (siehe Link unten). (automatisch geplant, erwartete Hörerzahl original: 80, fixe Veranstaltung: nein)

	Biomedizinische Signalanalyse Übung [BioSig-UE(A)] Dozent/in: Björn Eskofier Angaben: Übung, 2 SWS, ECTS: 2,5 Studienrichtungen / Studienfächer: WPF AI-MA ab 1 Inhalt: Im Rahmen der Vorlesung werden (a) die Grundlagen der Generation von wichtigen Biosignalen im menschlichen Körper (b) die Messung von Biosignalen und (c) Methoden zur Analyse von Biosignalen erläutert und dargestellt. (automatisch geplant, erwartete Hörerzahl original: 34, fixe Veranstaltung: nein)
		Do	16:15 - 17:45	EL 4.14		Eskofier, B.

Cognitive Neuroscience for AI Developers [CNAID(A)] Dozentinnen/Dozenten: Patrick Krauß, Andreas Kist, Andreas Maier Angaben: Vorlesung, 4 SWS, ECTS: 5, nur Fachstudium Termine: Mi, 10:15 - 11:45, H9 Do, 14:15 - 15:45, HG Studienrichtungen / Studienfächer: WPF AI-MA ab 1 Voraussetzungen / Organisatorisches: FAU students register for the written exam via meinCampus. https://www.studon.fau.de/crs3690005.html Inhalt: Neuroscience has played a key role in the history of artificial intelligence (AI), and has been an inspiration for building human-like AI, i.e. to design AI systems that emulate human intelligence. Neuroscience provides a vast number of methods to decipher the representational and computational principles of biological neural networks, which can in turn be used to understand artificial neural networks and help to solve the so called black box problem. This endeavour is called neuroscience 2.0 or machine behaviour. In addition, transferring design and processing principles from biology to computer science promises novel solutions for contemporary challenges in the field of machine learning. This research direction is called neuroscience-inspired artificial intelligence. The course will cover the most important works which provide the cornerstone knowledge to understand the biological foundations of cognition and AI, and applications in the areas of AI-based modelling of brain function, neuroscience-inspired AI and reverse-engineering of artificial neural networks. (automatisch geplant, erwartete Hörerzahl original: 200, fixe Veranstaltung: nein) Empfohlene Literatur: Gazzaniga, Michael. Cognitive Neuroscience - The Biology of the Mind. W. W. Norton & Company, 2018. Ward, Jamie. The Student's Guide to Cognitive Neuroscience. Taylor & Francis Ltd., 2019. Bermúdez, José Luis. Cognitive Science: An Introduction to the Science of the Mind. Cambridge University Press, 2014. Friedenberg, Jay D., and Silverman, Gordon W. Cognitive Science: An Introduction to the Study of Mind. SAGE Publications, Inc., 2015. Gerstner, Wulfram, et al. Neuronal dynamics: From single neurons to networks and models of cognition. Cambridge University Press, 2014.

Maschinelles Lernen für Zeitreihen [MLTS(A)] Dozentinnen/Dozenten: Björn Eskofier, , Dario Zanca, Luis Ignacio Lopera Gonzalez Angaben: Vorlesung, 2 SWS, benoteter Schein, ECTS: 2,5 Termine: Di, 08:15 - 09:45, 05.025 Studienrichtungen / Studienfächer: WPF AI-MA ab 1 Inhalt: Die Vorlesung vermittelt Konzepte des Maschinellen Lernens speziell im Hinblick auf Anwendungen bei Zeitreihen. Es handelt sich hier um eine Spezialisierungsvorlesung, eine erfolgreiche Absolvierung der Vorlesungen „IntroPR" und/oder „Pattern Recognition"/"Pattern Analysis" wird empfohlen. Konzepte, die in „IntroPR" vermittelt werden, werden hier als Grundwissen vorausgesetzt. Die folgenden Themen werden in der Vorlesung behandelt: Ein Überblick über die Anwendungsgebiete der Zeitreihenanalyse Methodische Grundlagen des Maschinellen Lernens (ML) für die Analyse von Zeitreihen, beispielsweise Gauß-Prozesse, Monte-Carlo Sampling und Deep Learning Design, Implementierung und Evaluation von ML Methoden, um Probleme in Zeitreihen zu adressieren Arbeitstechniken in bekannten Toolboxen zur Implementierung von relevanten Methoden, beispielsweise Tensorflow/Keras (automatisch geplant, erwartete Hörerzahl original: 30, fixe Veranstaltung: nein) Empfohlene Literatur: Machine Learning: A Probabilistic Perspective, Kevin Murphy, MIT press, 2012 The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Trevor Hastie, Robert Tibshirani, Jerome Friedman, Springer, 2009 Deep Learning, Ian Goodfellow and Yoshua Bengio and Aaron Courville, MIT Press, 2016 Reinforcement Learning: An Introduction, Richard S. Sutton and Andrew G. Barto, MIT press, 1998

Maschinelles Lernen für Zeitreihen Laborprojekt Dozentinnen/Dozenten: An Nguyen, Johannes Roider Angaben: Praktikum, 2 SWS, ECTS: 2,5 Termine: Zeit/Ort n.V. Studienrichtungen / Studienfächer: WPF AI-MA ab 1

Maschinelles Lernen für Zeitreihen Übung [MLTS-UE(A)] Dozentinnen/Dozenten: Leo Schwinn, Philipp Schlieper Angaben: Übung, 2 SWS, ECTS: 2,5 Termine: Mi, 10:15 - 11:45, 0.68 Studienrichtungen / Studienfächer: WPF AI-MA ab 1

Medical Image Processing for Diagnostic Applications (VHB-Kurs) Dozentinnen/Dozenten: Andreas Maier, Luis Carlos Rivera Monroy, Celia Martín Vicario, Manuela Meier, Arpitha Ravi Angaben: Vorlesung, 4 SWS, ECTS: 5 Termine: Zeit/Ort n.V. Studienrichtungen / Studienfächer: WPF AI-MA ab 1 Voraussetzungen / Organisatorisches: Requirements: mathematics for engineering Organization: This is an online course of Virtuelle Hochschule Bayern (VHB). Go to https://www.vhb.org to register to this course. FAU students register for the written exam via meinCampus. Inhalt: Medical imaging helps physicians to take a view inside the human body and therefore allows better treatment and earlier diagnosis of serious diseases. However, as straightforward as the idea itself is, so diversified are the technical difficulties to overcome when implementing a clinically useful imaging device. We begin this course by discussing all available modalities and the actual imaging goals which highly affect the imaging result. Some modalities produce very noisy results, but there are multiple other artifacts that show up in raw acquisition data and have to be dealt with. We address these issues in the chapter preprocessing and show how to compensate for image distortions, how to interpolate defect pixels, and finally correct bias fields in magnetic resonance images. The largest portion of this course covers the theory of medical image reconstruction. Here, from a set of projections from different viewing angles a 3-D image is merged that allows a definite localization of anatomical and pathological features. Following roughly the historical development of CT devices, we study the process from parallel beam to fan beam geometry and include a discussion of phantoms as a tool for calibration and image quality assessment. We then move forward and learn about reconstruction in 3-D. Since the system matrix often grows in dimensions such that many direct solvers become infeasible, we also discuss pros and cons of iterative methods. In the final chapter, image registration is introduced as the concept of computing the mapping that maps the content of one image to another. Two different acquisitions usually result in images that are at least rotated and translated against each other. Image registration forms the set of tools that we need to match certain image features in order to align both images for further processing, image improvement or image overlays. (erwartete Hörerzahl original: 100, fixe Veranstaltung: nein) Schlagwörter: Mustererkennung, Medizinische Bildverarbeitung

Medical Image Processing for Interventional Applications (VHB-Kurs) Dozentinnen/Dozenten: Andreas Maier, Luis Carlos Rivera Monroy, Celia Martín Vicario, Manuela Meier, Arpitha Ravi Angaben: Vorlesung, 4 SWS, ECTS: 5 Termine: Zeit/Ort n.V. Studienrichtungen / Studienfächer: WPF AI-MA ab 1 Voraussetzungen / Organisatorisches: mathematics for engineering; This lecture focuses on interventional procedures. It is recommended but not necessary to attend Medical Image Processing for Diagnostic Applications (MIPDA) before. Inhalt: This lecture focuses on recent developments in image processing driven by medical applications. All algorithms are motivated by practical problems. The mathematical tools required to solve the considered image processing tasks will be introduced. In addition to the lectures, we also offer exercise classes. The exercises consist of theoretical parts where you immerse in lecture topics. But we also set emphasis on the practical implementation of the methods. (erwartete Hörerzahl original: 100, fixe Veranstaltung: nein) Schlagwörter: Mustererkennung, Medizinische Informatik, Medizinische Bildverarbeitung

Middleware - Cloud Computing [MW(A)] Dozent/in: Tobias Distler Angaben: Vorlesung, 2 SWS, ECTS: 2,5 Termine: Do, 10:15 - 11:45, 0.031-113 Die Vorlesung findet in Präsenz statt und wird zusätzlich online übertragen. Studienrichtungen / Studienfächer: WPF AI-MA ab 1 Voraussetzungen / Organisatorisches: Geeignet für Bachelor ab 5. Semester, Master ab 1. Semester Empfohlene Grundlagenveranstaltung: Systemprogrammierung I Anmeldung zu den Übungen erforderlich

Erweiterte Übungen zu Middleware - Cloud Computing Dozentinnen/Dozenten: Laura Lawniczak, Tobias Distler Angaben: Übung, 2 SWS, ECTS: 5 Termine: Veranstaltung findet zusammen mit "Übungen zu Middleware - Cloud Computing" statt. Studienrichtungen / Studienfächer: WPF AI-MA ab 1

Rechnerübungen zu Middleware - Cloud Computing [RÜ MW] Dozentinnen/Dozenten: Tobias Distler, Laura Lawniczak Angaben: Übung, 2 SWS, Die Rechnerübung ist als Präsenzveranstaltung geplant, es wird aber (im selben Zeitslot) ebenfalls eine Onlinevariante geben Termine: Do, 14:00 - 16:00, 01.153-113 CIP Mo, 10:00 - 11:00, Zoom-Meeting Studienrichtungen / Studienfächer: WPF AI-MA ab 1 Voraussetzungen / Organisatorisches: Die Rechnerübung ist nur bei Bedarf betreut, ansonsten unbetreut. D.h. wenn zu Beginn der Übung kein Bedarf besteht findet die Übung unbetreut statt. Bei Fragen, die in der Übung nicht geklärt wurden, einfach bei uns vorbeischauen oder eine kurze e-Mail an i4mw-owner@lists.cs.fau.de schreiben.

Übungen zu Middleware - Cloud Computing Dozentinnen/Dozenten: Laura Lawniczak, Tobias Distler Angaben: Übung, 2 SWS, ECTS: 2,5 Termine: Die Tafelübungen werden asynchron als Videos angeboten. Mehr Informationen auf der Webseite und beim ersten Termin der Vorlesung. Studienrichtungen / Studienfächer: WPF AI-MA ab 1

The AMOS Project (VL) [OSS-AMOS-VL(A)] Dozent/in: Dirk Riehle Angaben: Vorlesung, 2 SWS, ECTS: 5, für FAU Scientia Gaststudierende zugelassen Termine: Mo, 10:15 - 11:45, H9 Studienrichtungen / Studienfächer: WPF AI-MA ab 1 Inhalt: This course teaches agile methods (Scrum and XP) and open source tools using a single semester-long project. Topics covered are: Agile methods and related software development processes Scrum roles, process practices, including product and engineering management Technical practices like refactoring, continuous integration, and test-driven development Principles and best practices of open source software development The project is a software development project in which each student team works with an industry partner who provides the idea for the project. This is a practical hands-on experience. Students can play one of two primary roles: Product owner. In this function, a student defines, prioritizes, communicates, and reviews requirements. The total effort adds up to 5 ECTS. Software developer. In this function, a student estimates their effort for requirements and implements them. The total effort adds up to 10 ECTS. Students will be organized into teams of 7-8 people, combining product owners with software developers. An industry partner will provide requirements to be worked out in detail by the product owners and to be realized by the software developers. The available projects will be presented in the run-up to the course. Class consists of a 90min lecture followed by a 90min team meeting. Rooms and times for team meetings are assigned at the beginning of the semester. You must be able to regularly participate in the team meetings. If you can't, do not sign up for this course. Students choosing the software developer role must have prior software development experience. Sign-up and further course information are available at https://amos.uni1.de - please sign up for the course on StudOn (available through previous link) as soon as possible. The course information will also tell you how the course will be held (online or in person). (automatisch geplant, erwartete Hörerzahl original: 50, fixe Veranstaltung: nein)

Wissensrepräsentation und -verarbeitung [WuV(A)] Dozentinnen/Dozenten: Florian Rabe, Michael Kohlhase Angaben: Vorlesung, 4 SWS, ECTS: 7,5, nur Fachstudium Termine: Di, 14:15 - 15:45, H6 Mi, 10:15 - 11:45, H6 Details auf https://kwarc.info/courses/wuv/ Studienrichtungen / Studienfächer: WPF AI-MA ab 1

Advanced Mechanized Reasoning in Coq [AMeRiCo(A)] Dozent/in: Tadeusz Litak Angaben: Vorlesung mit Übung, 4 SWS, ECTS: 7,5, geeignet als Schlüsselqualifikation Termine: Di, 08:15 - 09:45, 01.151-128 Do, 16:15 - 17:45, 01.151-128 Studienrichtungen / Studienfächer: WPF AI-MA ab 1 Schlagwörter: Coq Proof Assistants Mechanized Reasoning

Inertial Sensor Fusion [ISF(A)] Dozentinnen/Dozenten: Thomas Seel, Simon Bachhuber Angaben: Vorlesung mit Übung, 4 SWS, ECTS: 5 Termine: Di, 16:15 - 17:45, H10 Mi, 10:15 - 11:45, H10 Studienrichtungen / Studienfächer: WPF AI-MA ab 1

Kommunikation und Parallele Prozesse [KommPar(A)] Dozent/in: Lutz Schröder Angaben: Vorlesung mit Übung, 4 SWS, ECTS: 7,5 Termine: Di, 16:15 - 17:45, 02.133-113 Fr, 10:15 - 11:45, 02.133-113 Studienrichtungen / Studienfächer: WPF AI-MA ab 1

Logik-Basierte Sprachverarbeitung [LBS(A)] Dozentinnen/Dozenten: Michael Kohlhase, Florian Rabe Angaben: Vorlesung mit Übung, 4 SWS, ECTS: 5, nur Fachstudium, je nachdem was die Studierenden besser verstehen. Termine: Di, 16:15 - 17:45, 04.023 Do, 10:15 - 11:45, 04.023 https://www.studon.fau.de/crs4139317.html und https://kwarc.info/courses/lbs Studienrichtungen / Studienfächer: WPF AI-MA ab 1

Big Data Seminar Dozentinnen/Dozenten: Dominik Probst, Demian E. Vöhringer Angaben: Seminar, 2 SWS, ECTS: 5, nur Fachstudium Termine: nach Vereinbarung Studienrichtungen / Studienfächer: WF AI-MA ab 1 Schlagwörter: Big Data; Machine Learning; Predictive Maintenance

Virtual and Augmented Reality [VRAR] Dozentinnen/Dozenten: Daniel Roth, Tutoren, Gastredner Angaben: Sonstige Lehrveranstaltung, 8 SWS, benoteter Schein, ECTS: 10, nur Fachstudium, Combination of lecture (2 SWS) and practical course (6 SWS). Rückfragen, Verständnisfragen, sowie Prüfung können auch auf Deutsch abgelegt werden. --- Questions, comprehension questions, as well as examination can also be taken in German. Termine: Mi, 13:30 - 15:00, Zoom-Meeting Neben den regelmäßigen Zoom Meetings wird (je nach Corona Maßnahmen) „Open Lab“ Möglichkeiten unter Einhaltung Corona Maßnahmen geben. --- In addition to regular Zoom meetings, there will be (depending on Corona measures) "Open Lab" opportunities while adhering to Corona measures. Studienrichtungen / Studienfächer: WF AI-MA ab 1 WF AI-MA ab 1 Schlagwörter: Virtual Reality, Extended Reality, Augmented Reality