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Einrichtungen >> Technische Fakultät (TF) >> Department Informatik (INF) >>
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Lehrstuhl für Informatik 5 (Mustererkennung)
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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:
- Di, 14:15 - 15:45, 09.150
Do, 10:15 - 11:45, 09.150
- Studienrichtungen / Studienfächer:
- WPF MT-BA ab 5
WPF IuK-MA-MMS-INF ab 1
WPF ICT-MA-MPS ab 1
WPF INF-MA ab 1
WPF INF-BA-V-ME ab 1
PF CE-MA-TA-IT ab 1
WPF CME-MA ab 1
WPF MT-MA-BDV ab 1
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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Computer Vision [CV] -
- Dozentinnen/Dozenten:
- Vincent Christlein, Ronak Kosti
- Angaben:
- Vorlesung, 2 SWS, ECTS: 2,5, nur Fachstudium
- Termine:
- Mo, 8:15 - 9:45, H4
- Studienrichtungen / Studienfächer:
- WPF INF-MA ab 1
WF ICT-MA-MPS ab 1
WF CME-MA ab 1
WPF AI-MA ab 1
- Inhalt:
- 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.
Due to the unfortunate situation with the coronavirus (as of April 2020), it is not possible to start the course in the traditional face-to-face manner. We start with an 'inverted classroom' approach, where we pre-record lectures and upload them. Students are required to watch them before the actual lecture period. The actual lecture period (over Zoom) is dedicated to solving doubts and answering queries that students might have for the lectures watched.
- Empfohlene Literatur:
- Richard Szeliski: Computer Vision: Algorithms and Applications, Springer 2011.
Richard Hartley and Andrew Zisserman: Multiple view geometry in Computer Vision. Cambridge university press, 2003.
- Schlagwörter:
- computer vision; stereo vision; structure from motion; multi-view reconstruction; convolutional neural networks
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Datenbank Praxis [DBPraxis] -
- Dozent/in:
- Sebastian Wind
- Angaben:
- Vorlesung, 4 SWS, ECTS: 5, Online-Kurs im StudOn
- Termine:
- Online-Kurs im StudOn
- Studienrichtungen / Studienfächer:
- WPF INF-BA-V-SWE ab 4
WPF INF-BA-V-DB ab 4
WPF INF-MA ab 1
- Voraussetzungen / Organisatorisches:
- * Dies ist ein Online-Kurs, betreutes Eigenstudium! *
Keine formalen Voraussetzungen, grundlegende Kenntnisse im Bereich Datenbanken (zum Beispiel durch Besuch der Grundlagenvorlesungen KonzMod und IDB im Bachelor) werden empfohlen.
Der Kurs wird als Online-Kurs im Selbststudium angeboten. Die Kommunikation erfolgt per E-Mail und dem Forum im StudOn-Kurs. Ggf. wird ein Besprechungstermin vereinbart.
Der Kurs setzt die sichere Beherrschung einer Programmiersprache (z.B. Java) voraus, ebenso Erfahrung mit IDEs (Eclipse o. ä.). Erste Erfahrung im Umgang mit Mainframes (z/OS, TSO, ISPF) wäre hervorragend; z.B. Mainframe Programmierung I oder II.
- Inhalt:
- Datenbanken werden in fast jedem Unternehmen zur persistenten Datenspeicherung eingesetzt. Nach den Grundlagenvorlesungen im Bachelor, die die theoretische Einführung in die Datenbankwelt gegeben haben und die Basis für diesen Kurs bilden, wird in diesem Online-Kurs die praktische Erfahrung in der Arbeit mit einem Datenbanksystem in den Fokus gerückt. Der Online-Kurs ist so aufgebaut, dass es keine Vorlesungstermine und -videos gibt. Stattdessen kann der gesamte Inhalt in textueller Form über StudOn erarbeitet werden. Dies ermöglicht eine individuelle zeitliche Einteilung des Lernstoffs während der Vorlesungszeit.
Das in diesem Kurs verwendete Db2 for z/OS von IBM wird häufig im Enterprise-Umfeld eingesetzt. Insbesondere bei Banken, Versicherungsunternehmen und Softwarehäusern findet dieses Datenbanksystem Verwendung. Neben Oracle ist hier Db2 eines der weltweit am häufigsten eingesetzten Datenbanksysteme.
Die Kursinhalte umfassen:
Wiederholung der grundlegenden Konzepte aus den Bachelor-Pflichtvorlesungen Einführung und Überblick über Db2 for z/OS Administration von Db2 for z/OS Programmzugriff auf Db2 for z/OS Tools für Db2 for z/OS Angewandte Aufgaben anhand eines Praxisbeispiels
- Empfohlene Literatur:
- Ist im StudOn-Kurs verlinkt
- Schlagwörter:
- Mainframe, Programmierung, Programming, Administration, IBM, Datenbank, DB, Db2, Java, z, zOS
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Deep Learning [DL] -
- Dozent/in:
- Andreas Maier
- Angaben:
- Vorlesung, 2 SWS, ECTS: 2,5, nur Fachstudium, Information regarding the online teaching will be added to the studon course
- Termine:
- Di, 16:15 - 17:45, H4
- Studienrichtungen / Studienfächer:
- WPF ME-BA-MG6 4-6
WPF INF-MA ab 1
WPF MT-MA-BDV 1
WPF ME-MA-MG6 4-6
WPF AI-MA ab 1
- Voraussetzungen / Organisatorisches:
- The following lectures are recommended:
https://www.studon.fau.de/crs3729302.html
- Inhalt:
- 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.
- Empfohlene Literatur:
- 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)
- Schlagwörter:
- deep learning; machine learning
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Deep Learning Exercises [DL E] -
- Dozentinnen/Dozenten:
- Florian Thamm, Zijin Yang, Noah Maul, Karthik Shetty
- Angaben:
- Übung, 2 SWS, ECTS: 2,5, 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 added to the studon course
- Studienrichtungen / Studienfächer:
- WPF ME-BA-MG6 4-6
WPF INF-MA ab 1
WPF ME-MA-MG6 1-3
WPF AI-MA ab 1
- Schlagwörter:
- deep learning; machine learning
| | | | Mo | 12:00 - 14:00 | 0.01-142 CIP | |
Thamm, F. | |
| | | Di | 18:00 - 20:00 | 0.01-142 CIP | |
Thamm, F. | |
| | | Mi | 16:00 - 18:00 | 0.01-142 CIP | |
Thamm, F. | |
| | | Do | 14:00 - 16:00 | 0.01-142 CIP | |
Thamm, F. | |
| | | Fr | 8:00 - 10:00 | 0.01-142 CIP | |
Thamm, F. | |
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IT-Modernisierung [IT-Modern] -
- Dozent/in:
- Sebastian Wind
- Angaben:
- Vorlesung, 4 SWS, ECTS: 5, nur Fachstudium
- Termine:
- Mo, 8:15 - 11:45, 00.151-113
- Studienrichtungen / Studienfächer:
- WPF INF-MA ab 1
WPF INF-BA ab 4
WPF IIS-MA ab 1
WF ME-MA ab 1
- Voraussetzungen / Organisatorisches:
- * Lehrveranstaltung wird per Videokonferenz durchgeführt!*
* Auch im Fall, dass Präsenzveranstaltungen wieder erlaubt werden, wird NICHT in den Präsenzmodus gewechselt! *
Wir planen die Veranstaltung Mo von 8:15-11:45 Uhr. Exkursionen finden dieses Semester wegen der Pandemie nicht statt.
Bei Interessen bitte Anmeldung per StudOn.
Anrechenbar für anwendungsorientierte Säule.
- Inhalt:
- IT-Modernisierung beschäftigt sich mit dem Ersatz alter Software- und/oder Hardware. Software im kommerziellen Bereich hat eine typische Lebensdauer von über 25 Jahren, damit ist klar, dass diese keine der momentan oder zukünftig zur Verfügung stehenden Möglichkeiten nutzt oder nutzen kann, denn "damals" waren Single-CPUs der Standard und Vernetzung war unbekannt.
Durch das hohe Investitionsvolumen ist eine Neu-Programmierung praktisch nie wirtschaftlich sinnvoll und technisch oft unmöglich, da gar nicht genügend Programmierer zur Verfügung stehen.
Die Software hat aber einen hohen Reifegrad erreicht, so dass sich die Frage stellt, wie man diese auf neue Technologien umstellen kann.
Dieses Modul beleuchtet nun exemplarisch, auf welchen Feldern Bedarf besteht, wie der Stand der Technik ist, und welche zukünftigen Fragestellungen sich abzeichnen.
Die Studierenden werden durch Übungsaufgaben mit den "alten" Programmiersprachen wie Cobol uä. vertraut gemacht, und bearbeiten selbstständig kleine Aufgaben in Form eines Online-Kurses.
Momentane Planung (Themen nicht zwingend in dieser Reihenfolge):
Einleitung Überblick Aufbau (Architektur) eines Rechenzentrums DB2 unter z/OS Exkursion DATEV Rechenzentrum Exkursion Rechenzentrum einer Behörde Exkursion IBM Forschungslabor Böblingen Exkursion Fujitsu München RZ Konsolidierung Server Konsolidierung Cobol Grundlagen, RD/z, TSO/ISPF, JCL System z Hardware Grundlagen Java am Host Mainframe Programmierung Legacy-Anwendungen in einer Cloud-Architektur, CICS Modernisierung Internationalisierung: Unicode im Rechenzentrum Praxisbericht IT-Betrieb Infrastrukturen-Modernisierung usw.
Alle Exkursionen unter Vorbehalt einer Ersatzveranstaltung!
- Empfohlene Literatur:
- Alle Unterlagen werden über StudOn bereitgestellt.
- Schlagwörter:
- IT-Modernisierung, Fortran, Cobol, ABAP, Mainframe
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Kolloquium Magnetic Resonance Imaging [MRI] -
- Dozentinnen/Dozenten:
- Andreas Maier, Armin Nagel, Frederik Laun, Moritz Zaiß, Sebastian Bickelhaupt, David Grodzki
- Angaben:
- Kolloquium, 2 SWS, Das Kolloquium findet als Online-Veranstaltung statt. Zeit: Do 17:00 – 18:30 Uhr.
- Termine:
- Do, 17:00 - 18:30, Raum n.V.
- Studienrichtungen / Studienfächer:
- WPF INF-MA ab 1
- Schlagwörter:
- magnetic resonance imaging, mri
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Mainframe Programmierung I [MainProg I] -
- Dozent/in:
- Sebastian Wind
- Angaben:
- Vorlesung mit Übung, ECTS: 5
- Termine:
- Online-Kurs im StudOn
- Studienrichtungen / Studienfächer:
- WF INF-MA ab 1
- Voraussetzungen / Organisatorisches:
- * Der Kurs wird als Online-Kurs bei der VHB im StudOn im betreuten Selbststudium angeboten. *
Anrechenbar für die Säulen:
softwareorientiert anwendungsorientiert
- Inhalt:
- Der Begriff "Mainframe" bezeichnet grosse Rechenanlage, wie sie in der Wirtschaft für extrem grossen Anwendungen eingesetzt werden. Typische Branchen sind Banken und Versicherungen, aber auch Automobilhersteller und AI-Anwender.
Der Online-Kurs soll nun die Möglichkeit eröffnen, Erfahrungen mit der Programmierung eines Mainframes zu sammeln. Dazu gehören die elementaren Programmieraufgaben wie editieren, übersetzen, binden, laden, ausführen und debuggen, die anhand von Beispielen in der Programmiersprache CoBOL geübt werden.
Die Architektur der Mainframes werden sowohl aus Sicht der Rechnerarchitektur wie auch der Anwendersicht beleuchtet. Insbesondere werden die Virtualisierungsmöglichkeiten udn die gängigen Betriebssysteme wie z/OS und Linux auf den Mainframes behandelt.
Den Abschluss und Ausblick bildet die Datenhaltung und die Integration in die IT-Systemlandschaft.Inhalt:
0. Begrüßung und Einführung
1. CoBOL Programmierung
2. Einführung Mainframes
3. IBM Mainframe Architektur
4. z/OS
5. Anwendungsprogrammierung
6. Virtualisierung
7. Linux
8. Integration in die IT-Systemlandschaft
- Empfohlene Literatur:
- Wird über StudOn zur Verfügung gestellt.
- Schlagwörter:
- Mainframe, Programmierung, Cobol, Fortran, z, zOS, CICS, REX, Rational
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Mainframe Programmierung II [MainProg II] -
- Dozent/in:
- Sebastian Wind
- Angaben:
- Vorlesung mit Übung, 4 SWS, ECTS: 5, Online-Kurs
- Termine:
- Online-Kurs der VHB
- Studienrichtungen / Studienfächer:
- WF INF-BA-V-PS ab 4
WF INF-MA 1
- Voraussetzungen / Organisatorisches:
- * Der Kurs wird als Online-Kurs bei der VHB im StudOn im betreuten Selbststudium angeboten. *
Anrechenbar für die Säulen:
softwareorientiert anwendungsorientiert
- Inhalt:
- Aufbauend auf dem Modul Mainframe Programmierung I werden in diesem Kurs die Themen Virtualisierung, Online Transaction Processing und die Anwendung von Datenbanken behandelt. Darüber hinaus werden erweiterte Cobol Konzepte beleuchtet und die Anwendung der Skriptsprache JCL vermittelt. Der Kurs Mainframe Programmierung II wird online abgehalten und bietet den Studierenden die Möglichkeit sich durch Selbststudium neue Kompetenzen anzueignen, sowie das neu gewonnene Wissen anhand von Screencasts einzuüben.
Lern- bzw. Methodenkompetenz
Das Modul vermittelt sowohl Kompetenzen im selbst organisierten Lernen, wie auch Erfahrungen mit einer multi-modalen Lernumgebung.
- Schlagwörter:
- Mainframe, Programmierung, Programming, Unternehmensdatenverarbeitung, Enterprise Computing
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Mainframe@Home [MFH] -
- Dozent/in:
- Sebastian Wind
- Angaben:
- Vorlesung mit Übung, 4 SWS, ECTS: 5, Online-Kurs im StudOn
- Termine:
- Online-Kurs im StudOn
- Studienrichtungen / Studienfächer:
- WPF INF-BA ab 4
WPF INF-MA ab 1
- Voraussetzungen / Organisatorisches:
- Der Kurs wird als Online-Kurs im StudOn im betreuten Selbststudium angeboten.
Zur Kommunikation mit den Kurs-Team steht ein Forum zur Verfügung. Außerdem kann Kontakt per E-Mail aufgenommen werden. Der Kurs setzt grundlegende Kenntnisse der Informatik voraus. Außerdem wird ein Verständnis für die Implementierung von Algorithmen benötigt. Anrechenbar für die Säulen:
softwareorientiert anwendungsorientiert
- Inhalt:
- Großrechner sind das Herzstück der weltweiten IT-Landschaft. Durch die hohe Verfügbarkeit und geringe Ausfallquote werden Mainframes in sehr großen Firmen verwendet. Die Transaktionszahlen für die Datenverarbeitung sind bei diesen Unternehmen außerdem sehr hoch. Mit diesem Kurs soll Ihnen die Möglichkeit geboten werden, sich mit der Programmierung von Anwendungen für und der Arbeit mit Großrechner zu beschäftigen. Sie verwenden in diesem Kurs eine eigene Mainframe-Emulation auf Ihrem Rechner und arbeiten mit dieser in verschiedenen Übungsaufgaben.
Behandelt werden die folgenden Kapitel:
Einführung in das Thema Großrechner Virtualisierung Multiple Virtual Storage (MVS) Common Business Oriented Language (Cobol) Formula Translator (Fortran) Restructured Extended Executor (Rexx) Virtual Storage Access Method (VSAM) Java und Unix auf dem Mainframe
- Empfohlene Literatur:
- Auf die Literatur wird in der jeweiligen Lerneinheit im StudOn hingewiesen.
- Schlagwörter:
- Mainframe, Programmierung, zOS Betriebssystem, Rechner, Computer
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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 INF-MA ab 1
WPF INF-BA-V-ME ab 5
PF CE-MA-TA-IT ab 1
WPF IuK-MA-MMS-INF ab 1
WPF ICT-MA-MPS 1-4
WPF MT-MA-BDV ab 1
WPF MT-BA ab 5
WF CME-MA 1-4
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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Medizintechnik II [MT2] -
- Dozent/in:
- Andreas Maier
- Angaben:
- Vorlesung, 4 SWS, ECTS: 3,75, für Anfänger geeignet, 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. The exercises will start in the second week (19.-23.4.). The lecture videos can be found at https://www.video.uni-erlangen.de/course/id/1022 and all further information can be found on studon https://www.studon.fau.de/crs2961834.html
- Termine:
- Mi, 12:15 - 13:45, H4
Do, 8:15 - 9:45, H4
- Studienrichtungen / Studienfächer:
- PF MT-BA 2
- Inhalt:
- Die Vorlesung MT2 richtet sich an Studierende des Studiengangs Medizintechnik und zählt dort zu den Grundlagenvorlesungen im Bereich Informatik. Methoden und Geräte, welche die Anatomie und Funktion des Körpers für die Diagnose und Therapie aufarbeiten und darstellen, werden erklärt. Ein Schwerpunkt liegt auf dem Verständnis und der Anwendung von Grundalgorithmen der medizinischen Bildverarbeitung, wie beispielsweise Segmentierung, Filterung und Bildrekonstruktion. Die vorgestellten Modalitäten beinhalten Röntgensysteme, Computertomographie (CT), Magnetresonanztomographie (MRT), Optische Kohärenztomographie (OCT) und Ultraschall (US).
- Empfohlene Literatur:
- Olaf Dössel: Bildgebende Verfahren in der Medizin: Von der Technik zur medizinischen Anwendung, Springer, 1999.
Arnulf Oppelt: Imaging Systems for Medical Diagnostics, Publicis Kommunikations AG, Erlangen, 2005 Medical Imaging Systems - An Introductory Guide (Open Access) https://link.springer.com/book/10.1007%2F978-3-319-96520-8
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Pattern Analysis [PA] -
- Dozent/in:
- Christian Riess
- Angaben:
- Vorlesung, 3 SWS, benoteter Schein, ECTS: 3,75, 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
- Termine:
- Di, Fr, 12:15 - 13:45, H16
- Studienrichtungen / Studienfächer:
- WPF ME-BA-MG6 4-6
PF MT-MA-BDV 1-4
WPF IuK-MA-MMS-INF 1-4
WPF ICT-MA-MPS 1-4
WPF CME-MA 1-4
WF CME-MA 1-4
WPF INF-MA 1-4
WPF CE-MA-INF ab 1
WF ASC-MA 1-4
WPF ME-MA-MG6 1-3
WPF AI-MA ab 1
- Voraussetzungen / Organisatorisches:
- 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.
- Inhalt:
- 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.
To participate, please join the Pattern Analysis studOn class: https://www.studon.fau.de/crs3708405_join.html
- Empfohlene Literatur:
- 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
- Schlagwörter:
- pattern recognition, pattern analysis
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Projekt Computer Vision [ProjCV] -
- Dozentinnen/Dozenten:
- Vincent Christlein, Martin Mayr
- Angaben:
- Praktikum, ECTS: 10, 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
- Termine:
- Mo, 12:00 - 14:00, Übung 3 / 01.252-128, 00.156-113 CIP
- Studienrichtungen / Studienfächer:
- WPF INF-MA ab 1
WPF MT-MA ab 1
- Voraussetzungen / Organisatorisches:
- Basic knowledge of image processing is desirable. In the first session there will be a short recap on image representation and basic image filtering techniques. However, having visited lectures such as Introduction to Pattern Recognition (IntroPR) or Diagnostic Medical Image Processing (DMIP) might prove beneficial.
Please contact us if you have any questions. You can register via Studon (https://www.studon.fau.de/crs3668979.html) for the Computer Vision Project. During the semester lecture and exercise alternate on a weekly basis. Exercises are supervised and take place in one of the CIP pools. All exercises must be completed. You can get either 5 or 10 ECTS credits for this project. The following options are available:
5 ECTS (counts as: Hochschulpraktikum)
This option requires:
lectures (strongly recommended as they introduce the background required for the exercises) exercises (in groups of 2 people) need to be finished on time individual presentation about a state-of-the-art research paper at the end of the semester (graded if needed)
10 ECTS (counts as Hochschulpraktikum (5 ECTS) + Forschungspraktikum (5 ECTS), or Master Project Computer Science (10 ECTS))
lectures (strongly recommended as they introduce the background required for the exercises) exercises (in groups of 2 people) need to be finished on time individual coding/research project under supervision of a LME PhD student at the end of regular schedule (graded if needed)
Important: You cannot use the lecture/exercise part as a 5 ECTS research project (Forschungspraktikum). Please contact one of the PhD students at the lab if you need a research project.
- Inhalt:
- This project gives you the chance to learn about current computer vision topics and get practical experience in the field during the exercises.
Last semester, the following topics were covered:
- Schlagwörter:
- Master Project, Pattern Recognition, Computer Vision
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Projekt Flat-Panel CT Reconstruction [ProjFCR] -
- Dozentinnen/Dozenten:
- Yixing Huang, Jennifer Maier
- Angaben:
- Praktikum, ECTS: 10, 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. No registration is required to attend this course. All further information will be provided in the StudOn course (link below).
- Termine:
- Di, 10:00 - 12:00, 0.01-142 CIP
ab 13.4.2021
- Studienrichtungen / Studienfächer:
- WPF INF-MA ab 1
WPF MT-MA ab 3
- Inhalt:
- The aim of this master project is to build a state-of-the-art flat-panel CT reconstruction software. The project is designed in two parts: The first part is the Academic Laboratory (Hochschulpraktikum). These 5 ECTS can be earned by attending the course, finishing the exercises and giving a short presentation at the end of the semester. The second part is the 5 ECTS Research Laboratory (Forschungspraktikum), where after the semester the students can work on research topics related to the topics taught in the course.
In the Academic Laboratory, the basics of CT reconstruction will be developed in a group. All participants will create a basic CT reconstruction pipeline that is able to reconstruct flat-panel CT images.
The following topics will be taught and implemented in this course:
In the Research Laboratory, the participants will be asked to adopt the designed pipeline individually to specific problems in CT reconstruction. These topics are always related to current research at the Pattern Recognition Lab, including for example:
You will incorporate your work into a fully-fledged CT reconstruction and analysis tool that makes it easy to evaluate the reconstruction algorithms. At the end of the project, a trip to the Siemens Healthineers in Forchheim is planned in order to experiment with a real scanner.
- Schlagwörter:
- Master Project, Pattern Recognition, CT Reconstruction
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Seminar AI for Healthcare: Challenges in Translating Promises into Patient Outcomes [AIOutcomes] -
- Dozentinnen/Dozenten:
- Katharina Breininger, Mathias Unberath, Nishant Ravikumar
- Angaben:
- Seminar, 2 SWS, ECTS: 5, This course will be conducted online. Registration will be enabled via StudOn starting in May. If you are interested in attending the seminar, please send an email to katharina.breininger@fau.de.
- Studienrichtungen / Studienfächer:
- WPF INF-MA ab 1
WPF AI-MA ab 1
WPF MT-MA-BDV ab 1
- Voraussetzungen / Organisatorisches:
- FAU students register for the course in StudOn. Registration will be enabled via StudOn starting in May. If you are interested in attending the seminar, please send an email to katharina.breininger@fau.de.
The seminar is offered as a compact course during summer intersession in September (exact dates are tbd).
This course is offered under the prerequisite that the corresponding funding for Prof. Dr.-Ing. Mathias Unberath and Dr. Nishant Ravikumar is granted by FAU.
- Inhalt:
- Artificial Intelligence in general, and machine learning (AI/ML) in particular, have become a major thrust of healthcare research. Concisely, it is now widely accepted that learning-based approaches will be a core building block of personalized and precision medicine. The reasons for this are twofold: First, these methods either automate data analysis tasks that would be intractable otherwise thus paving the way for innovative decision making; and second, they offer recommendations in high-variance decisions based on population-scale evidence used for their development, thus potentially decoupling provider experience and outcomes.
Unfortunately, most of the recent successes on private in house or public grand challenge data have been linked to neither improved outcomes nor clinical impact but are limited to task-based comparisons in sandbox settings. Furthermore, developed techniques that have been validated thoroughly in a research setting often fail/perform poorly in clinical ones, and do not account for inherent biases in the data and/or experimental setup.
In this seminar, we will review recently published research on AI/ML for healthcare that successfully translated into clinical practice to identify key factors in study design, method development, infrastructure, or regulation that enable translation.
The seminar will focus on three distinct areas: digital pathology, medical image computing, and computer-aided interventions. Where possible, guest lectures from academia, clinics, as well as industry will be invited as part of the seminar.Students will be able to
Students will have acquired competences to
perform an unstructured literature review on an assigned subject
independently research the assigned subject
present and introduce the subject to their peers
give a scientific presentation in English according to international conference standards
summarize their findings in a written report that adheres to good scientific practice
The overall grade consists of two parts: A 30-minute seminar presentation (50% final grade, comprised of content and delivery). The goal of the seminar is to prepare a topic for other students in an accessible way. After all groups have presented their topics, we will break out into smaller teams to further process the seminar talk contents and synergize them into a paper-style report and report-out (conference-style) presentation (~4 pages IEEE and 10 minutes, respectively; 50% final grade, comprised of content and delivery) that discusses at least one core challenge identified throughout the seminar and proposes community guidelines to improve translation of AI research into clinical practice. Talks and seminar paper should be in English.
Students will work in groups of two if the number of participants allows.
- Empfohlene Literatur:
- Unberath, M., Ghobadi, K., Levin, S., Hinson, J., & Hager, G. D. (2020). Artificial Intelligence‐Based Clinical Decision Support for COVID-19–Where Art Thou?. Advanced Intelligent Systems, 2(9), 2000104.
Christopher J. Kelly, Alan Karthikesalingam, Mustafa Suleyman, Greg Corrado & Dominic King: Key challenges for delivering clinical impact with artificial intelligence, BMC Medicine, Vol. 17, Article number: 195 (2019)
Adam Bohr and Kaveh Memarzadeh (eds.): Artificial Intelligence in Healthcare, Academic Press (2020)
Herein for example:
Sara Gerke, Timo Minssen, Glenn Cohen: Chapter 12 - Ethical and legal challenges of artificial intelligence-driven healthcare, Adam Bohr, Kaveh Memarzadeh, (eds.), Artificial Intelligence in Healthcare, Academic Press, pp. 295-336 (2020)
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Seminar Artificial Intelligence and Neuroscience [SemAINeuro] -
- Dozentinnen/Dozenten:
- Andreas Maier, Patrick Krauß, Joachim Hornegger
- Angaben:
- Seminar, 2 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, 8:15 - 9:45, KH 1.021
- Studienrichtungen / Studienfächer:
- WPF INF-MA ab 1
WPF MT-MA-BDV ab 1
WPF CE-MA-TA-MT ab 1
WPF AI-MA ab 1
- Voraussetzungen / Organisatorisches:
- Registrierung via StudOn:
https://www.studon.fau.de/crs3687384.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. Furthermore, 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.
In addition, 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.
Finally, the idea of combining artificial intelligence, in particular deep learning, and computational modelling with neuroscience and cognitive science has recently gained popularity, leading to a new research paradigm for which the term cognitive computational neuroscience has been coined. There is increasing evidence that, even though artificial neural networks lack biological plausibility, they are nevertheless well suited for modelling brain function.
The seminar will cover the most important works which provide the cornerstone knowledge to understand cutting edge research at the intersection of AI and neuroscience.Students will be able to • independently identify challenges in translating technical solutions from the bench to the bedside, and assess how close to clinical feasibility a technical solution is Students will have acquired competences to • perform an unstructured literature review on an assigned subject
• independently research the assigned subject
• present and introduce the subject to their peers
• give a scientific presentation in English according to international conference standards
• summarize their findings in a written report that adheres to good scientific practice
- Empfohlene Literatur:
- Barak, O. (2017). Recurrent neural networks as versatile tools of neuroscience research. Current opinion in neurobiology, 46, 1-6.
Barrett, D. G., Morcos, A. S., & Macke, J. H. (2019). Analyzing biological and artificial neural networks: challenges with opportunities for synergy?. Current opinion in neurobiology, 55, 55-64.
Cichy, R. M., Khosla, A., Pantazis, D., Torralba, A., & Oliva, A. (2016). Comparison of deep neural networks to spatio-temporal cortical dynamics of human visual object recognition reveals hierarchical correspondence. Scientific reports, 6(1), 1-13.
Cichy, R. M., & Kaiser, D. (2019). Deep neural networks as scientific models. Trends in cognitive sciences, 23(4), 305-317.
Dasgupta, S., Stevens, C. F., & Navlakha, S. (2017). A neural algorithm for a fundamental computing problem. Science, 358(6364), 793-796.
Hassabis, D., Kumaran, D., Summerfield, C., & Botvinick, M. (2017). Neuroscience-inspired artificial intelligence. Neuron, 95(2), 245-258.
Kriegeskorte, N., & Douglas, P. K. (2018). Cognitive computational neuroscience. Nature neuroscience, 21(9), 1148-1160.
Marblestone, A. H., Wayne, G., & Kording, K. P. (2016). Toward an integration of deep learning and neuroscience. Frontiers in computational neuroscience, 10, 94.
Nasr, K., Viswanathan, P., & Nieder, A. (2019). Number detectors spontaneously emerge in a deep neural network designed for visual object recognition. Science advances, 5(5), eaav7903.
Rahwan, I., Cebrian, M., Obradovich, N., Bongard, J., Bonnefon, J. F., Breazeal, C., ... & Wellman, M. (2019). Machine behaviour. Nature, 568(7753), 477-486.
Yamins, D. L., & DiCarlo, J. J. (2016). Using goal-driven deep learning models to understand sensory cortex. Nature neuroscience, 19(3), 356-365.
Jonas, E., & Kording, K. P. (2017). Could a neuroscientist understand a microprocessor?. PLoS computational biology, 13(1), e1005268.
- Schlagwörter:
- algorithms; medical image processing
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Seminar Digital Pathology and Deep Learning [SemDP] -
- Dozentinnen/Dozenten:
- Katharina Breininger, Christian Marzahl, Andreas Maier, Samir Jabari, Ingmar Blümcke
- Angaben:
- Seminar, 2 SWS, ECTS: 5, This course will be held online until further notice. Please register via in StudOn starting from March 15, 2021. Note that equal chances for all applicants apply until March 27, midnight.
- Termine:
- Di, 16:30 - 18:00, 02.133-113
- Studienrichtungen / Studienfächer:
- WPF MT-MA 1
WPF MT-MA-BDV 1
WPF INF-MA 1
- Inhalt:
- Pathology is the study of diseases and aims to deliver a fine-grained diagnosis to understand processes in the body as well as to enable targeted treatment. In this area, the opportunities for digital image processing are vast: While the need for precision medicine, i.e., taking into account various co-dependencies when formulating the best possible treatment for a patient, is high, the number of pathologists ist not increasing accordingly. Deep learning-based techniques can be used for different objectives in this scope. Examples include screening large microscopy images for specific rare events, providing visual augmentation with analysis data. Additionally, the availability of massive data collections, including genomics and further biological factors, can be utilized to determine specific information about diseases that were previously unavailable.
This seminar is offered to students of medicine as well as computer sciences and medical engineering and similar. Students will have to present a topic from this field in a short (30 min) and comprehensive presentation.List of topics:
Staining and special stains (including immunohistochemistry, enzyme-based dyes and tissue microarrays)
Current computational pathology
Knowledge/Feature fusion into a diagnosis
Histopathology quality control
Data sets as limiting factor - limits of current data sets
Large scale / clinical grade solutions
Computational and augmented tumor grading
In vivo microstructural analysis
Big data in pathology (multi-omics)
Histology image registration
Staining differences and stain normalization
Transfer learning and domain adaptation
Explainable AI
Virtual staining
Digital workflow in Germany vs. the world
Limits of digital pathology
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Seminar Intraoperative Imaging and Machine Learning [IIML] -
- Dozentinnen/Dozenten:
- Katharina Breininger, Holger Kunze, Holger Keil
- Angaben:
- Seminar, 2 SWS, ECTS: 5, This course will be held online until further notice. Please register via in StudOn starting from March 15, 2021. Note that equal chances for all applicants apply until March 27, midnight.
- Termine:
- Mi, 8:30 - 10:00, 09.150
- Studienrichtungen / Studienfächer:
- WPF MT-MA-BDV ab 1
WPF INF-MA ab 1
WPF ICT-MA ab 1
WPF CE-MA-INF ab 1
- Inhalt:
- For many applications, techniques like deep learning allow for considerably faster algorithm development and allow to automate tasks that were performed manually in the past. In medical imaging, a large variety of time-consuming tasks that interfere with clinical workflows has the potential for automation. However, at the same time new challenges arise like data privacy regulations and ethics concerns.
In this seminar, we want to develop an application that allows for the automation of an X-ray based intraoperative planning or measurement procedure from a holistic perspective. To this end, we will invite a surgeon to explain the medical background and visit the operating room to understand the surgeons’ needs while performing the task. Having understood the underlying medical problem, we will look into topics of data privacy, code of ethics, prototype development, and UI design for surgeons. Furthermore, we will touch regulatory requirements necessary for releasing software to clinics.
At the end of the seminar, the students will have developed and documented a prototypical application for the indented intraoperative use case.
Students will be able to
visit an operation room, following the rules of such an environment
perform their own literature research on a given subject
independently research this subject according to data privacy and ethical standard
present and introduce the subject to their student peers
give a scientific talk in English according to international conference standards
describe their results in a scientific report
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Seminar – Road Scene Understanding for the Visually Impaired [SemRSUVI] -
- Dozentinnen/Dozenten:
- Hakan Calim, Andreas Maier
- Angaben:
- Seminar, 2 SWS, ECTS: 5
- Termine:
- Fr, 8:15 - 9:30, 09.150
- Inhalt:
- Understanding road scenes and creating maps from urban environments is a challenging task in Computer Vision. It is used in self-driving cars, robotics and assistive navigation systems for blind or visually impaired pedestrians.
The topic of this seminar will be to build a software tools that will enable the creation of a navigation assistant for the blind and visually impaired. In particular the following issues will be addressed:
1. Define the hardware resources (e.g. computational resources, camera/cell phone, TOF/Lidar/focal length, sensors, ...)
2. Set up a web-based collaborative labeling environment using crowd-based labeling tools (e.g. EXACT (https://github.com/ChristianMarzahl/Exact))
3. define guidelines for crowdsourced labeling
4. Define use cases (face recognition, license plates) for managing and anonymizing the data.
The aim of this project seminar is to build tools that will enable preparation of data, e.g. image segmentation of roads, sidewalks and obstacles, etc. In a second step, the data should be ready for the creation of maps and respective annotations from the scenes so it can be used building for an assistive navigation system for blind or visually impaired pedestrians.
- Empfohlene Literatur:
- J. C. Chang, S. Amershi, E. Kamar, Revolt: Collaborative Crowdsourcing for Labeling Machine Learning Datasets, CHI 2017, Denver, CO, USA, May 6-11, 2017, http://library.usc.edu.ph/ACM/CHI%202017/1proc/p2334.pdf
Oana Inel, Khalid Khamkham, Tatiana Cristea et al., CrowdTruth: Machine-Human Computation Framework for Harnessing Disagreement in Gathering Annotated Data, International Semantic Web Conference, ISWC 2014: The Semantic Web - ISWC 2014, pp 486 504, https://link.springer.com/chapter/10.1007/978-3-319-11915-1_31
Brody Huval, Tao Wang, Sameep Tandon et al., An Empirical Evaluation of Deep Learning on Highway Driving, Cornell University, Apr. 7, 2015, https://arxiv.org/abs/1504.01716
Joel Pazhayampallil, Free Space Detection with Deep Nets for Autonomous Driving, http://cs231n.stanford.edu/reports/2015/pdfs/jpazhaya_final.pdf
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Speech and Language Understanding [SLU] -
- Dozent/in:
- Andreas Maier
- Angaben:
- Vorlesung, 2 SWS, benoteter Schein, ECTS: 5, nur Fachstudium
- Termine:
- Mi, 16:15 - 17:45, 01.151-128
- Studienrichtungen / Studienfächer:
- WPF INF-BA-V-ME 5-6
WPF INF-MA ab 1
WPF AI-MA ab 1
- Voraussetzungen / Organisatorisches:
- https://www.studon.fau.de/crs3717775.html
Für diese Lehrveranstaltung ist eine Anmeldung erforderlich.
Die Anmeldung erfolgt über: StudOn
- Inhalt:
- 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.
- Empfohlene Literatur:
- 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
- Schlagwörter:
- Mustererkennung, Merkmale, HMM, Sprachmodelle, Prosodie, Suchalgorithmen
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Voice-enabled healthcare [VEH] -
- Dozent/in:
- Björn Heismann
- Angaben:
- Vorlesung, ECTS: 2,5, nur Fachstudium
- Termine:
- Do, 8:15 - 9:45, Raum n.V.
Further information will be provided on StudOn
- Studienrichtungen / Studienfächer:
- WPF MT-MA ab 1
- Voraussetzungen / Organisatorisches:
- Master-Studenten MT Semester 1-3 (und andere interessierte Fachrichtungen)
- Inhalt:
- Voice recognition, speech synthesis, sentiment analysis and natural language processing are groundbreaking technologies for improved human machine interactions. This seminar intends to give students the opportunity to get in touch with the latest technologies in this space and venture out on a literature review or prototype building journey to improve healthcare applications. The seminar features a lecture part where participants are introduced to the algorithmic background of voice and natural language processing. You are enabled to analyze literature and / or develop own prototypes of voice-enabled healthcare applications. Potential fields of application include e.g. voice-controlled interventional devices and sentiment analysis for psychiatric diseases.
Objectives:
Understand science of voice recognition and natural language processing
Understand medical human interactions and medical needs
Analyze combinations of voice technologies and potential applications in medicine
Skills:
Algorithmic background of voice recognition and NLP
Literature analysis and prototype building
Advanced knowledge: Medical technology
Basic knowledge: Medicine
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