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Vorlesungs- und Modulverzeichnis nach Studiengängen >> Technische Fakultät (Tech) >> Artificial Intelligence (AI) >>
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Lehrveranstaltungsverzeichnis Masterstudiengang Artificial Intelligence (AI)
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Seminar Graphische Datenverarbeitung [GraHS] -
- Dozentinnen/Dozenten:
- Tobias Günther, Tim Weyrich
- Angaben:
- Masterseminar, 2 SWS, benoteter Schein, ECTS: 5, nur Fachstudium
- Termine:
- Mo, 10:15 - 11:45, 01.151-128
- Studienrichtungen / Studienfächer:
- WPF AI-MA ab 1
- Voraussetzungen / Organisatorisches:
- Für dieses Seminar besteht Anwesenheitspflicht, wobei maximal drei Fehlstunden erlaubt sind. Bei medizinischen Gründen oder anderen besonderen Vorkommnissen werden Ausnahmen gemacht, sofern diese vor dem Termin vereinbart worden sind.
- Inhalt:
- Dieses Seminar befasst sich mit fortgeschrittenen Themen im Visual Computing und umfasst sowohl bahnbrechende als auch aktuelle Forschungspublikationen. Die Themen beinhalten unter anderem Animation, Rendering, Materialien, Fabrication, und Capturing.
- Schlagwörter:
- Computer Graphics, Visual Computing, Rendering
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Master-Projekt Datenmanagement [MastProj] -
- 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
- Schlagwörter:
- Master; Projekt; Project; Masterprojekt; Master Projekt; EDEN; BATS; TDQMed; DSAM
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Fantastic datasets and where to find them [FANDAT] -
- Dozent/in:
- Andreas Kist
- Angaben:
- Seminar, 2 SWS, benoteter Schein, ECTS: 2,5, nur Fachstudium
- Termine:
- Do, 13:15 - 14:45, Zoom-Meeting
Online only seminar, please register via StudOn to have access to the Zoom link
ab 28.10.2021
- 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.
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Maschinelles Lernen und Datenanalytik für Industrie 4.0 [MADI40] -
- Dozentinnen/Dozenten:
- Björn Eskofier, An Nguyen, Johannes Roider, Christoph Scholl
- Angaben:
- Seminar, 2 SWS, benoteter Schein, ECTS: 5, nur Fachstudium, Registration via mail to johannes.roider@fau.de
- Termine:
- Mi, 16:15 - 18:00, 00.010
Starts October 20th 2021
- Studienrichtungen / Studienfächer:
- WPF AI-MA ab 1
- Voraussetzungen / Organisatorisches:
- Prerequisites
Registration via e-mail to johannes.roider@fau.de Registration period: 16.08. - 19.10.2021
The seminar is planned to be held in person, given that the Bavarian state government allows face-to-face teaching at the time when the course starts. More information will be sent via mail to registered students. 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
Examination:
50% of grade: Presentation + demo (20 minutes)
50% of grade 4 pages IEEE standard paper (excluding references) (+ code submission)
Attending the presentations of other students
- 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 (provided or open source datasets). 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
Students will learn to research and present a topic within the context of machine learning and data analytics for Industry 4.0 / healthcare independently
Students will learn to identify opportunities, challenges and limitations of corresponding ML approaches for Industry 4.0 / healthcare
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
- Empfohlene Literatur:
- Literature (Selection)
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
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Seminar Humans in the Loop: The Design of Interactive AI Systems [SemHitL] -
- 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
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
Adaptive Learning
- 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
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Project Representation Learning [PRL] -
- Dozent/in:
- Bernhard Kainz
- Angaben:
- Sonstige Lehrveranstaltung, 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 chair for health data science. Students may also propose their own projects, which will be coordinated and refined with the module lead during preliminary discussions.
- 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/
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Cognitive Neuroscience for AI Developers [CNAID] -
- Dozentinnen/Dozenten:
- Patrick Krauß, Andreas Kist, Andreas Maier
- Angaben:
- Vorlesung, 4 SWS, ECTS: 5, nur Fachstudium, This course will be held online until the coronavirus pandemic is contained to such an extent that the Bavarian state government can allow face-to-face teaching again. Information regarding the online teaching will be provided in the studon course.
- Termine:
- Mo, 16:15 - 17:45, H14
Di, 16:15 - 17:45, H4
- 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.
- 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.
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Maschinelles Lernen für Zeitreihen [MLTS] -
- Dozentinnen/Dozenten:
- Björn Eskofier, Oliver Amft, Dario Zanca, Luis Ignacio Lopera Gonzalez
- Angaben:
- Vorlesung, 2 SWS, benoteter Schein, ECTS: 2,5
- Termine:
- Di, 12:15 - 13:45, 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
- 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
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Medical Image Processing for Diagnostic Applications (VHB-Kurs) [MIPDA] -
- Dozentinnen/Dozenten:
- Andreas Maier, Tristan Gottschalk, Celia Martín Vicario, Julian Hoßbach
- 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.
- Schlagwörter:
- Mustererkennung, Medizinische Bildverarbeitung
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Algorithmic Bioinformatics -
- Dozentinnen/Dozenten:
- David B. Blumenthal, und Mitarbeiter/innen
- Angaben:
- Vorlesung mit Übung, 4 SWS, benoteter Schein, ECTS: 5
- Termine:
- Mi, 14:00 - 16:00, H15
Fr, 12:00 - 14:00, H16
Lecture: Wednesday, 14:00 – 16:00, Exercise: Friday: 12:00 – 14:00
- Studienrichtungen / Studienfächer:
- WPF AI-MA ab 1
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Inertial Sensor Fusion [ISF] -
- Dozentinnen/Dozenten:
- Thomas Seel, Simon Bachhuber
- Angaben:
- Vorlesung mit Übung, 4 SWS, ECTS: 5
- Termine:
- Di, 14:15 - 15:45, H10
Mi, 12:15 - 13:45, H10
Einzeltermin am 11.2.2022, 10:00 - 12:00, H5, 01.030
- Studienrichtungen / Studienfächer:
- WPF AI-MA ab 1
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Deep Learning in Multimedia Forensics [DLMFor] -
- Dozent/in:
- Christian Riess
- Angaben:
- Praktikum, ECTS: 10
- Termine:
- Vorbesprechung: Donnerstag, 28.10.2021, 16:00 - 18:00 Uhr
- Studienrichtungen / Studienfächer:
- WF AI-MA ab 1
- Voraussetzungen / Organisatorisches:
- Participants must bring some practical experience in python.
Experience with the implementation of deep neural networks helps, but is not strictly necessary.
- Inhalt:
- Subtle traces in the processing history of an image or video can provide a clue on the recording device, or whether some editing was applied. Multimedia forensics investigates methods to extract these traces from the data. Recent methods in multimedia forensics use deep learning to better adapt to data from the internet.
In this project, participants will gather practical experience with deep learning methods in multimedia forensics. Participants will implement published methods from scratch, and do own performance investigations on selected example inputs.
On the first meeting on October 28, groups of two students will be formed, and tasks will be distributed. During the project, there are regular consultation hours for status updates and programming support.
| | | | Di | 12:00 - 14:00 | 00.156-113 CIP | |
Riess, Ch.
Lorch, B. | |
| First meeting: October 28, 16:00h in room 12.155 (Martensstr. 3, 12th floor) |
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