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course list >> Technische Fakultät (Tech) >> Artificial Intelligence (AI) >>
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Lehrveranstaltungsverzeichnis Masterstudiengang Artificial Intelligence (AI)
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Seminar Inverse Rendering [InvHS] -
- Lecturers:
- Tim Weyrich, Bernhard Egger, Marc Stamminger
- Details:
- Masterseminar, 2 cred.h, graded certificate, ECTS: 5, nur Fachstudium
- Dates:
- Tue, 8:15 - 9:45, 01.151-128
- Fields of study:
- WPF AI-MA ab 1
- Prerequisites / Organisational information:
- Attendance is mandatory for this seminar, with a maximum of two absences allowed. Exceptions will be made for medical reasons or other special occurrences, provided they have been agreed upon prior to the appointment.
- Contents:
- This seminar covers advanced topics in computer graphics and computer vision and includes both ground-breaking and recent research publications. Topics include inverse rendering, appearance, surface reflectance, and computer graphics in general.
- Keywords:
- Inverse Rendering, Appearance, Surface Reflectance, Computer Graphics, Digital Reality, Visual Computing, Rendering
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Biomedical Image Analysis Project [BIMAP] -
- Lecturers:
- Andreas Kist, René Groh, und Mitarbeiter/innen
- Details:
- Projektseminar, 4 cred.h, ECTS: 10, nur Fachstudium, für FAU Scientia Gaststudierende zugelassen
- Dates:
- Thu, 11:15 - 12:45, room tbd
AIBE Seminar Room, Werner-von-Siemens-Str. 61, 91054 Erlangen
- Fields of study:
- WPF AI-MA ab 1
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Master-Projekt Datenmanagement [MastProj] -
- Lecturers:
- Richard Lenz, Alle Assistenten
- Details:
- Projektseminar, 2 cred.h, ECTS: 10, nur Fachstudium
- Dates:
- unregelmäßig, nach Bedarf
- Fields of study:
- WPF AI-MA 3
- Contents:
- 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
- Keywords:
- Master; Projekt; Project; Masterprojekt; Master Projekt; EDEN; BATS; TDQMed; DSAM
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Fantastic datasets and where to find them [FANDAT] -
- Lecturers:
- Andreas Kist, René Groh
- Details:
- Seminar, 2 cred.h, graded certificate, ECTS: 2,5, nur Fachstudium, für FAU Scientia Gaststudierende zugelassen
- Dates:
- Thu, 13:15 - 14:45, Zoom-Meeting
Online only seminar, please register via StudOn to have access to the Zoom link
- Fields of study:
- WPF AI-MA ab 1
- Contents:
- 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.
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Green AI - AI for Sustainability and Sustainability of AI [GREENAI] -
- Lecturers:
- Eva Dorschky, René Raab, Björn Eskofier
- Details:
- Seminar, 2 cred.h, graded certificate, ECTS: 5, für FAU Scientia Gaststudierende zugelassen, There are no more free places in the SS 2022.
- Dates:
- Thu, 10:15 - 11:45, 00.010
single appointment on 21.7.2022, 10:15 - 11:45, 01.151-128
- Fields of study:
- WPF AI-MA ab 1
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Machine Learning and Data Analytics for Industry 4.0 [MADI40] -
- Lecturers:
- Björn Eskofier, Johannes Roider, Christoph Scholl, Lukas Schmidt
- Details:
- Seminar, 2 cred.h, graded certificate, ECTS: 5, nur Fachstudium, für FAU Scientia Gaststudierende zugelassen, Registration via mail to johannes.roider@fau.de
- Dates:
- Wed, 16:15 - 18:00, 00.010
Starts April 27th 2022
- Fields of study:
- WPF AI-MA ab 1
- Prerequisites / Organisational information:
- 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.
- Contents:
- 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
- Recommended literature:
- 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.
- Keywords:
- Machine Learning, Data Analytics, Process Mining, Predictive Maintenance, Industry 4.0, Healthcare, Automotive
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Tracking Olympiad [TRACO] -
- Lecturers:
- Andreas Kist, René Groh, Tutoren
- Details:
- Seminar, 4 cred.h, ECTS: 5, nur Fachstudium
- Dates:
- Tue, 10:15 - 11:45, room tbd
Fri, 9:15 - 10:45, room tbd
AIBE Seminar Room, Werner-von-Siemens-Str. 61, 91054 Erlangen
- Fields of study:
- WPF AI-MA ab 1
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Project Representation Learning [PRL] -
- Lecturers:
- Bernhard Kainz, Johanna Müller, Mischa Dombrowski
- Details:
- Sonstige Lehrveranstaltung, 8 cred.h, ECTS: 10, nur Fachstudium
- Dates:
- to be determined
- Fields of study:
- WPF AI-MA ab 1
- Prerequisites / Organisational information:
- 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
- Contents:
- 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.
- Recommended literature:
- 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/
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The AMOS Project (UE) [OSS-AMOS-UE] -
- Lecturer:
- Dirk Riehle
- Details:
- Übung, für FAU Scientia Gaststudierende zugelassen
- Fields of study:
- WPF AI-MA ab 1
- Contents:
- 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 student teams work 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 in the beginning of the semester. 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).
| | | Mon | 14:15 - 15:45 | Übung 3 / 01.252-128 | |
Riehle, D. | |
| | Tue | 08:15 - 09:45 | 00.152-113 | |
Riehle, D. | |
| | Wed | 14:15 - 15:45 | 01.255-128 | |
Riehle, D. | |
| | Thu | 08:15 - 09:45 | Übung 3 / 01.252-128 | |
Riehle, D. | |
| | Thu | 12:15 - 13:45 | Übung 3 / 01.252-128 | |
Riehle, D. | |
| | Thu | 16:15 - 17:45 | Übung 3 / 01.252-128 | |
Riehle, D. | |
| | Fri | 08:15 - 09:45 | Übung 3 / 01.252-128 | |
Riehle, D. | |
| | Fri | 12:15 - 13:45 | Übung 3 / 01.252-128 | |
Riehle, D. | |
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Artificial Intelligence II [AI II] -
- Lecturer:
- Michael Kohlhase
- Details:
- Vorlesung, 4 cred.h, für FAU Scientia Gaststudierende zugelassen, Artificial Intelligence II will take place this semester, probably online. For details, see the announcements at https://fsi.cs.fau.de/forum/149-Kuenstliche-Intelligenz-II
- Dates:
- Tue, 16:15 - 17:45, H8
Thu, 12:15 - 13:45, H8
- Fields of study:
- WPF AI-MA ab 1
WPF AI-MA ab 1
- Contents:
- Artificial Intelligence II will be hybrid (presence/online) this semester. Details will be announced on StudOn
Dieser Kurs beschäftigt sich mit den Grundlagen der Künstlichen Intelligenz (KI), insbesondere mit Techniken des Schliessens unter Unsicherheit, des maschinellen Lernens und dem Sprachverstehen.
Der Kurs baut auf der Vorlesung Künstliche Intelligenz I vom Wintersemester auf und führt diese weiter. Lernziele und Kompetenzen
Fach- Lern- bzw. Methodenkompetenz
Wissen: Die Studierenden lernen grundlegende Repräsentationsformalismen und Algorithmen der Künstlichen Intelligenz kennen.
Anwenden: Die Konzepte werden an Beispielen aus der realen Welt angewandt (Übungsaufgaben).
Analyse: Die Studierenden lernen über die Modellierung in der Maschine menschliche Intelligenzleistungen besser einzuschätzen.
Sozialkompetenz
- Recommended literature:
- Die Vorlesung folgt weitgehend dem Buch
Stuart Russell und Peter Norvig: Artificial Intelligence: A Modern Approach. Prentice Hall, 3rd edition, 2009.
Deutsche Ausgabe:
Stuart Russell und Peter Norvig: Künstliche Intelligenz: Ein Moderner Ansatz. Pearson-Studium, 2004 (Übersetzung der 2.Auflage).
ISBN: 978-3-8273-7089-1
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Cognitive Neuroscience for AI Developers [CNAID] -
- Lecturers:
- Patrick Krauß, Andreas Kist, Andreas Maier
- Details:
- Vorlesung, 4 cred.h, ECTS: 5, nur Fachstudium
- Dates:
- Tue, 12:15 - 13:45, H8
Thu, 8:15 - 9:45, H11
- Fields of study:
- WPF AI-MA ab 1
- Prerequisites / Organisational information:
- FAU students register for the written exam via meinCampus.
https://www.studon.fau.de/crs4053784_join.html
- Contents:
- 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.
- Recommended literature:
- 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.
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Computational Photography and Capture [CPaC] -
- Lecturer:
- Tim Weyrich
- Details:
- Vorlesung, 2 cred.h, graded certificate, ECTS: 5, nur Fachstudium
- Dates:
- Tue, 12:15 - 13:45, 00.152-113
- Fields of study:
- WPF AI-MA ab 1
- Contents:
- Never in human history have we been able to record so much of our environment in so little time with such high quality. Since the rise of smartphones, nearly everyone carries a powerful camera with them in their daily lives.
This module introduces the theoretical and practical aspects of modern photography and capture algorithms: universal models of colour, computer-controlled cameras, lighting and shape capture.
The lecture covers the following topics:
Cameras, sensors and colour
Image processing (e.g., blending, warping)
Radiometry
Appearance acquisition
Structured-light 3D acquisition
Image-based and video-based rendering
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Computer Vision [CV] -
- Lecturers:
- Bernhard Egger, Andreas Maier, Tim Weyrich
- Details:
- Vorlesung, 2 cred.h, ECTS: 2,5, nur Fachstudium
- Dates:
- Thu, 12:15 - 13:45, H4
- Fields of study:
- WPF AI-MA ab 1
- Contents:
- This lecture discusses important algorithms from the field of computer vision. The emphasis lies on 3-D vision algorithms, covering the geometric foundations of computer vision, and central algorithms such as stereo vision, structure from motion, optical flow, and 3-D multiview reconstruction. The course will also introduce Convolutional Neural Networks (with some examples to play around) and discuss it's importance and impact. Participants of this advanced course are expected to bring experience from prior lectures either from the field of pattern recognition or from the field of computer graphics.
- Recommended literature:
- Richard Szeliski: Computer Vision: Algorithms and Applications, Springer 2011.
Richard Hartley and Andrew Zisserman: Multiple view geometry in Computer Vision. Cambridge university press, 2003.
- Keywords:
- computer vision; stereo vision; structure from motion; multi-view reconstruction; convolutional neural networks
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Computer Vision Exercise [CV-E] -
- Lecturers:
- Bernhard Egger, Shih-Yuan Huang, Sarma Jeet Sen, Maximilian Weiherer, Mathias Zinnen, Darius Rückert
- Details:
- Übung, 2 cred.h, ECTS: 2,5, nur Fachstudium, Exercises are voluntary and will not be graded/corrected. The exam will contain questions on the excercises.
- Fields of study:
- WPF AI-MA ab 1
- Keywords:
- computer vision; stereo vision; structure from motion; multi-view reconstruction; convolutional neural networks
| | | Tue | 10:00 - 12:00 | 0.01-142 CIP | |
Egger, B. | |
| | Wed | 8:00 - 10:00 | 00.156-113 CIP | |
Egger, B. | |
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Deep Learning [DL] -
- Lecturer:
- Andreas Maier
- Details:
- Vorlesung, 2 cred.h, ECTS: 2,5, nur Fachstudium, für FAU Scientia Gaststudierende zugelassen, Information regarding the online teaching will be added to the studon course
- Dates:
- Fri, 10:15 - 11:45, H4
- Fields of study:
- WPF AI-MA ab 1
- Prerequisites / Organisational information:
- The following lectures are recommended:
https://www.studon.fau.de/crs4449450.html
- Contents:
- Deep Learning (DL) has attracted much interest in a wide range of applications such as image recognition, speech recognition and artificial intelligence, both from academia and industry.
This lecture introduces the core elements of neural networks and deep learning, it comprises:
(multilayer) perceptron, backpropagation, fully connected neural networks
loss functions and optimization strategies
convolutional neural networks (CNNs)
activation functions
regularization strategies
common practices for training and evaluating neural networks
visualization of networks and results
common architectures, such as LeNet, Alexnet, VGG, GoogleNet
recurrent neural networks (RNN, TBPTT, LSTM, GRU)
deep reinforcement learning
unsupervised learning (autoencoder, RBM, DBM, VAE)
generative adversarial networks (GANs)
weakly supervised learning
applications of deep learning (segmentation, object detection, speech recognition, ...)
The accompanying exercises will provide a deeper understanding of the workings and architecture of neural networks.
- Recommended literature:
- Ian Goodfellow, Yoshua Bengio, Aaron Courville: Deep Learning. MIT Press, 2016
Christopher Bishop: Pattern Recognition and Machine Learning, Springer Verlag, Heidelberg, 2006
Yann LeCun, Yoshua Bengio, Geoffrey Hinton: Deep learning. Nature 521, 436–444 (28 May 2015)
- Keywords:
- deep learning; machine learning
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Human Computer Interaction [HCI] -
- Lecturer:
- Björn Eskofier
- Details:
- Vorlesung, 3 cred.h, ECTS: 3,75
- Dates:
- Tue, Thu, 8:15 - 9:45, H10
Die erste Veranstaltung findet am 28.04 um 08:15 in H10 statt.
- Fields of study:
- WPF AI-MA ab 1
- Prerequisites / Organisational information:
- Folien zur Vorlesung und Organisation über Studon.
- Contents:
- Studon Kurs:
https://www.studon.fau.de/studon/goto.php?target=crs_4380069
- Keywords:
- human-computer interaction, Mensch-Maschine-Schnittstelle, grafische Benutzerschnittstellen, mobile Mensch-Computer-Interaktion, Mensch-Maschine-Interaktion im Fahrzeug, ubiquitäre und eingebettete interaktive Systeme
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Human Computer Interaction Exercises [HCI-E] -
- Lecturer:
- Madeleine Flaucher
- Details:
- Übung, 1 cred.h, ECTS: 1,25
- Dates:
- Tue, 12:15 - 13:45, H3 Egerlandstr.3
- Fields of study:
- WPF AI-MA ab 1
- Keywords:
- human-computer interaction, Mensch-Maschine-Schnittstelle, grafische Benutzerschnittstellen, mobile Mensch-Computer-Interaktion, Mensch-Maschine-Interaktion im Fahrzeug, ubiquitäre und eingebettete interaktive Systeme
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Medical Image Processing for Diagnostic Applications (VHB course) [MIPDA] -
- Lecturers:
- Andreas Maier, Luis Carlos Rivera Monroy, Celia Martín Vicario, Arpitha Ravi
- Details:
- Vorlesung, 4 cred.h, ECTS: 5
- Dates:
- to be determined
- Fields of study:
- WPF AI-MA ab 1
- Prerequisites / Organisational information:
- 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.
- Contents:
- 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.
- Keywords:
- Mustererkennung, Medizinische Bildverarbeitung
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Computational Neurotechnology [Neurotech] -
- Lecturer:
- Tobias Reichenbach
- Details:
- Übung, 2 cred.h, ECTS: 2,5, nur Fachstudium, The password for joining the course on StudOn is: Neurotech22. The course will start with the first lecture on Thursday, 28th of April 2022.
- Dates:
- Tue, 8:15 - 9:45, H6
- Fields of study:
- WPF AI-MA 2
- Keywords:
- Neurotechnologie, neuroengineering, neurotechnology, computational, neuroscience, brain-machine-interface, brain-computer interface, neurorehabilitation
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Parallel Systems [PSys] -
- Lecturers:
- Frank Hannig, Jürgen Teich
- Details:
- Vorlesung, 2 cred.h, ECTS: 2,5, nur Fachstudium
- Dates:
- Tue, 12:15 - 13:45, 01.021
- Fields of study:
- WPF AI-MA ab 1
- Contents:
- Selbst unser PC erlaubt bereits ein hohes Maß an nebenläufiger Verarbeitung von Daten. Die effiziente Ausnutzung von Parallelität bedarf allerdings auch spezieller Programmier- und Übersetzungstechniken. Beschrieben werden Eigenschaften unterschiedlicher paralleler Rechnerarchitekturen und Metriken zu deren Beurteilung. Weiterhin werden Modelle und Sprachen zum Programmieren paralleler Rechner eingeführt. Neben der Programmierung von allgemeinen Parallelrechnern werden Entwurfsmethoden (CAD) vorgestellt, wie man ausgehend von einer algorithmischen Problemstellung massiv parallele Rechenfelder in VLSI herleiten kann. Im Einzelnen werden behandelt:
1. Theorie der Parallelität (parallele Computermodelle, parallele Spezifikationsformen und -sprachen, Performanzmodelle und -berechnung)
2. Klassifikation paralleler und skalierbarer Rechnerarchitekturen (Multiprozessoren und Multicomputer, Vektorrechner, Datenflussmaschinen, VLSI-Rechenfelder)
3. Programmierung paralleler Rechner (Sprachen und Modelle, Entwurfsmethoden und Compiler, Optimierung)
4. Massive Parallelität: Vom Algorithmus zur Schaltung Theoretische und praktische Übungen mit rechnergestützten Werkzeugen
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Erweiterte Übungen zu Parallele Systeme [EU-PSys] -
- Lecturers:
- Stefan Groth, Michael Witterauf, Marcel Brand, Frank Hannig
- Details:
- Übung, 2 cred.h, ECTS: 2,5, für FAU Scientia Gaststudierende zugelassen
- Dates:
- Thu, 14:00 - 18:00, 02.133-128
Wed, 8:00 - 12:00, 02.133-128
single appointment on 22.6.2022, 9:00 - 13:00, 02.133-128
single appointment on 24.6.2022, 14:00 - 18:00, 02.133-128
single appointment on 13.7.2022, 9:00 - 13:00, 02.133-128
single appointment on 15.7.2022, 14:00 - 18:00, 02.133-128
single appointment on 20.7.2022, 9:00 - 13:00, 02.133-128
single appointment on 22.7.2022, 14:00 - 18:00, 02.133-128
verpflichtend, vor Ort an den Rechnerarbeitsplätzen des Lehrstuhls
- Fields of study:
- WPF AI-MA ab 1
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Pattern Analysis [PA] -
- Lecturer:
- Christian Riess
- Details:
- Vorlesung, 3 cred.h, graded certificate, ECTS: 3,75, This course will be held as inverted classroom with physical meetings, with a "best-effort" online option.
- Dates:
- Thu, 16:15 - 17:45, H16
Fri, 12:15 - 13:45, Zoom-Meeting
- Fields of study:
- WPF AI-MA ab 1
- Prerequisites / Organisational information:
- Please join the class "Pattern Analysis" in studOn. All lecture material will be linked and made available there.
It is recommended (but not mandatory) that participants attend the lecture Pattern Recognition first.
- Contents:
- This lecture complements the lectures "Introduction to Pattern Recognition" and "Pattern Recognition".
In this third edition, we focus on analyzing and simplifying feature representations.
Major topics of this lecture are density estimation, clustering, manifold learning, hidden Markov models, conditional random fields, and random forests.
The lecture is accompanied by exercises, where theoretical results are
practically implemented and applied.
All materials (for lecture and exercises) can be found in the associated studOn class at https://www.studon.fau.de/crs4398245.html
To participate in Pattern Analysis, please join this studOn class. You can use this registration link: https://www.studon.fau.de/crs4398245_join.html
- Recommended literature:
- Christopher Bishop: Pattern Recognition and Machine Learning, Springer Verlag, Heidelberg, 2006
T. Hastie, R. Tibshirani, J. Friedman: The Elements of Statistical Learning, 2nd edition, Springer Verlag, 2017.
Antonio Criminisi and J. Shotton: Decision Forests for Computer Vision and Medical Image Analysis, Springer, 2013
- Keywords:
- pattern recognition, pattern analysis
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Speech Understanding [SLU] -
- Lecturers:
- Seung Hee Yang, Alexander Barnhill, Andreas Maier
- Details:
- Vorlesung, 2 cred.h, graded certificate, ECTS: 5, nur Fachstudium
- Dates:
- Mon, 14:00 - 16:00, Hörsaal ZMPT
- Fields of study:
- WPF AI-MA ab 1
- Prerequisites / Organisational information:
- https://www.studon.fau.de/crs4464784.html
Für diese Lehrveranstaltung ist eine Anmeldung erforderlich.
Die Anmeldung erfolgt über: StudOn
- Contents:
- Nach Behandlung der grundlegenden Mechanismen menschlicher Spracherzeugung und Sprachwahrnehmung
gibt die Vorlesung eine detaillierte Einführung in (vornehmlich) statistisch orientierte Methoden der
maschinellen Erkennung gesprochener Sprache.
Schwerpunktthemen sind Merkmalgewinnung, Vektorquantisierung,
akustische Sprachmodellierung mit Hilfe von Markovmodellen, linguistische Sprachmodellierung mit Hilfe
stochastischer Grammatiken, prosodische Information
sowie Suchalgorithmen zur Beschleunigung des Dekodiervorgangs.
- Recommended literature:
- Niemann H.: Klassifikation von Mustern; Springer, Berlin 1983
Niemann H.: Pattern Analysis and Understanding; Springer, Berlin 1990
Schukat-Talamazzini E.G.: Automatische Spracherkennung;
Vieweg, Wiesbaden 1995
Rabiner L.R., Schafer R.: Digital Processing of Speech Signals;
Prentice Hall, New Jersey 1978
Rabiner L.R., Juang B.H.: Fundamentals of Speech Recognition;
Prentice Hall, New Jersey 1993
- Keywords:
- Mustererkennung, Merkmale, HMM, Sprachmodelle, Prosodie, Suchalgorithmen
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Swarm Intelligence (SI), formerly Organic Computing (OC) [SI] -
- Lecturer:
- Rolf Wanka
- Details:
- Vorlesung, 2 cred.h, ECTS: 2,5, für FAU Scientia Gaststudierende zugelassen, Also for CE; Formerly known as Organic Computing (OC)
- Dates:
- Thu, 14:15 - 15:45, H15
- Fields of study:
- WPF AI-MA ab 1
- Contents:
- Unter Swarm Intelligence (SI) versteht man den Entwurf und den Einsatz von selbst-organisierenden Systemen, die sich den jeweiligen Umgebungsbedürfnissen dynamisch anpassen. Diese Systeme zeichnen sich dadurch aus, dass sie die sog. Self-*-Eigenschaft besitzen, d.h. sie sind selbst-konfigurierend, selbst-optimierend, selbst-heilend, selbst-schützend, selbst-erklärend, ...
Als Vorbild für solche technischen Systeme werden Strukturen und Methoden biologischer und anderer natürlicher Systeme gewählt.
- Recommended literature:
- Ch. Müller-Schloer, Ch. von der Malsburg, R. P. Würt. Organic Computing. Informatik-Spektrum, Band 27, Nummer 4, S. 332-336. (LINK)
I. C. Trelea. The particle swarm optimization algorithm: convergence analysis and parameter selection. Information Processing Letters 85 (2003) 317-325. (LINK)
J. M. Kleinberg. Authoritative sources in a hyperlinked environment. Journal of the ACM 46 (1999) 604-632. (LINK)
M. Dorigo. V. Maniezzo. A Colorni. Ant system: an autocatalytic optimizing process. Technical Report 91-016, Politecnico di Milano, 1991. (LINK)
A. Badr. A. Fahmy. A proof of convergence for Ant algorithms. Information Sciences 160 (2004) 267-279.
M. Clerc. J. Kennedy. The particle swarm - Explosion, stability, and convergence in a multidimensional complex space. IEEE Transactions on Evolutionary Computation 8 (2002) 58-73.
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The AMOS Project (VL) [OSS-AMOS-VL] -
- Lecturer:
- Dirk Riehle
- Details:
- Vorlesung, 2 cred.h, ECTS: 5, für FAU Scientia Gaststudierende zugelassen
- Dates:
- Wed, 10:15 - 11:45, 00.151-113
- Fields of study:
- WPF AI-MA ab 1
- Contents:
- 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 student teams work 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 in the beginning of the semester. 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).
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Visual Computing in Medicine 2 [VCMed2] -
- Lecturers:
- Thomas Wittenberg, Peter Hastreiter
- Details:
- Vorlesung, ECTS: 2,5, für FAU Scientia Gaststudierende zugelassen
- Dates:
- Thu, 12:15 - 13:45, H10
- Fields of study:
- WPF AI-MA ab 1
- Prerequisites / Organisational information:
- Until further notice, this course will take place in electronic form. Detailed information is provided in the StudOn-course "Visual Computing in Medicine 2 (VCMed 2) - summer term 2020".
Bis auf Weiteres findet der Kurs in elektronischer Form statt. Weitere Informationen finden Sie im StudOn-Kurs "Visual Computing in Medicine 2 (VCMed 2) - summer term 2020".
- Contents:
- Building onto the lecture VCMed1, VCMed2 provides examples of concrete solutions for diagnosis and therapy planning based on complex clinical images. It provides information how basic methods are selected and combined into practical applicable concepts. Examples from clinical applications will be used to relate strategies and requirements in clinical practice as well as the development process. Additionally, methods of medical image analysis and visualization are discussed in detail.
Linking methods of medical image analysis and visualization for processing diagostic and interventional questions
Providing algorithmic approaches with concrete solution strategies for the processing of clinical iamges from the perspective of medical needs
Overview of various medical imaging domains
Multimodal image registration with non-rigid transformations
Current topics of image-based diagnosis and therapy planning
- Recommended literature:
- P.M. Schlag, S. Eulenstein, Th. Lange „Computerassistierte Chirurgie", Elsevier Verlag 2010
H. Handels, „Medizinische Bildverarbeitung, Bildanalyse, Mustererkennung und Visualisierung für die computergestützte ärztliche Diagnostik und Therapie", Vieweg und Teubner Verlag, 2009
B. Preim, D. Bartz, "Visualization in Medicine - Theory, Algorithms, and Applications”, Morgan Kaufmann Verlag, 2007
E. Neri, D. Caramella, C. Bartolozzi, „Image Processing in Radiology", Springer Verlag, 2008
Th. Lehmann, W. Oberschelp, E. Pelikan, R. Pepges, „Bildverarbeitung für die Medizin", Springer Verlag, 1997
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Logic-Based Reprsentation for Mathematical/Technical Knowledge [KRMT] -
- Lecturers:
- Michael Kohlhase, Florian Rabe
- Details:
- Vorlesung mit Übung, 4 cred.h, ECTS: 5, für FAU Scientia Gaststudierende zugelassen, * The course will be in presence, but streamed at https://fau.zoom.us/j/65839665250 and recorded at https://www.fau.tv/course/id/3065
- Dates:
- Tue, 10:15 - 11:45, 02.133-113
Wed, 16:15 - 17:45, 02.133-113
We'll try to do the course in person. If necessary, any zoom links will be announced here.
- Fields of study:
- WPF AI-MA ab 1
- Prerequisites / Organisational information:
- We'll try to do the course in person. If necessary, any zoom links will be announced here.
- Contents:
- Grundlagen der Mathematik, Modulare Formalisierung in Theoriegraphen, Narrative Strukturen in informellen mathematisch/technischen Dokumenten, Formalisierung von Logiksprachen in Metalogiken.
Lernziele und Kompetenzen:
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Neuartige Rechnerarchitekturen [NeuRa] -
- Lecturers:
- Philipp Holzinger, Simon Pfenning, Dietmar Fey
- Details:
- Hauptseminar, 4 cred.h, ECTS: 5
- Dates:
- Tue, 12:15 - 13:45, 07.150
- Fields of study:
- WF AI-MA ab 1
- Contents:
- Die Entwicklung moderner CPUs hat eine interessante Evolution durchlaufen. Angefangen bei einfachen Single-Core CPUs wurde zunächst die Taktschraube immer weiter nach oben gedreht. Als dies aus thermischem Grund nicht weiter möglich war, wurden Parallelrechner aus ihrer akademischen Nische vertrieben und zum Allgemeingut eines jeden Informatikers. Neuere Entwicklungen zeigen nun den Einsatz von heterogenen Rechnerarchitekturen, also die Verbindung verschiedener Recheneinheiten wie CPUs, GPUs, FPGAs, um mittels Spezialhardware anfallende Aufgaben schneller und energieeffizienter lösen zu können. Neueste Forschungsansätze hingegen versuchen nun auch den Hauptspeicher eines Rechners "intelligent" zu machen und Prozessoren direkt in den Speicher zu integrieren - sogenanntes in- oder near-memory-Computing.
Ziel dieses Moduls ist das Kennen, Verstehen, Verwenden, Vergleichen,und Evaluieren
verschiedener Rechnerarchitekturen von der Multi-Core CPU bis zum FPGA-Near-Memory-Beschleuniger. Anhand praktischer Anwendungen (z.B. Neuronale Netze, Bildverarbeitung, Autonomes Fahren) können die Architekturen erprobt werden.
Hierzu wird jedem Teilnehmenden ein Thema/Architektur zur Bearbeitung übertragen, welche sie/er selbstständig wissenschaftlich in einer schriftlichen Ausarbeitung und didaktisch in einem Vortrag aufarbeitet und präsentiert.Fachkompetenz
Wissen
Lernende können Wissen über die Grundprinzipien moderner Rechnerarchitekturen (Intel, ARM CPUs; AMD, Nvidia GPUs; FPGAs, Beschleunigerkerne) wiedergeben. Verstehen
Lernende verstehen die Grundprinzipien der Datenverarbeitung der einzelnen Architekturen; im Speziellen verstehen sie ob und warum eine vorgegebene Architektur besonders gut für die Lösung eines Problems geeignet ist.
Lernende verstehen die unterschiedlichen Ansätze zum Parallelismus der vorgestellten Architekturen. Anwenden
Lernende sind in der Lage Anwendungen auf den vorgegebenen Architekturen z.B. durch Programmierung umzusetzen. Hierzu erklären Studierende wie die Parallelisierungstechniken in bestehenden Architekturen eingesetzt werden. Evaluieren (Beurteilen)
Lernende evaluieren die Eignung von Architekturen, um bestimmte Probleme effizient auf diese Abbilden zu können. Sozialkompetenz
Lernende können komplexe fachbezogene Inhalte klar und zielgruppengerecht präsentieren und eigene Standpunkte in einer Fachdiskussion argumentativ vertreten.
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Information Visualization [InfoVIS] -
- Lecturer:
- Roberto Grosso
- Details:
- Vorlesung, 2 cred.h, graded certificate, ECTS: 2,5, nur Fachstudium, für FAU Scientia Gaststudierende zugelassen, Please, check the StudOn course InfoVIS
- Dates:
- Thu, 14:15 - 15:45, 01.150-128
- Fields of study:
- WF AI-MA ab 1
- Contents:
- In diesem Modul werden Visualisierungsmethoden für die folgenden Datentypen behandelt:
Multivariate Daten
Graphen und Netzwerke
Dynamische Graphen
Hierarchien und Bäume
Time-Series Daten
Textvisualisierung
Für mehr Information über Inhalt dieses Moduls besuche Sie den StudOn-Kurs InfoVIS (https://www.studon.fau.de/crs2722847.html).
- Recommended literature:
- Robert Spence: Information Visualization: Design for Interaction
Stuart K. Card, Jock Mackinlay, Ben Shneiderman: Readings in Information Visualization – Using Vision to Think
Benjamin B. Bederson, Ben Shneiderman: The Craft of Information Visualization – Readings and Reflections
Tamara Munzner: Visualization Analysis and Design: Principles, Techniques, and Practice
- Keywords:
- visualization, JavaScript, graphs, hierarchies, D3 programming, text visualization
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Compressive Sensing [CompSense] -
- Lecturer:
- Ali Bereyhi
- Details:
- Vorlesung mit Übung, 4 cred.h, ECTS: 5, nur Fachstudium
- Dates:
- Tue, 14:15 - 15:45, 05.025
Wed, 16:15 - 17:45, 05.025
single appointment on 20.5.2022, 16:15 - 17:45, 05.025
single appointment on 13.6.2022, 12:15 - 13:45, 05.025
- Fields of study:
- WF AI-MA ab 1
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