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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
- Termine:
- Di, 12:15 - 13:45, H8
Do, 8:15 - 9:45, H11
- Studienrichtungen / Studienfächer:
- WPF MT-BA ab 5
WF IuK-MA-MMS-INF ab 1
WF ICT-MA ab 1
WPF INF-MA ab 1
WPF INF-BA-V-ME 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/crs4053784_join.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:
- Bernhard Egger, Andreas Maier, Tim Weyrich
- Angaben:
- Vorlesung, 2 SWS, ECTS: 2,5, nur Fachstudium
- Termine:
- Do, 12:15 - 13:45, H4
- Studienrichtungen / Studienfächer:
- WPF ME-BA-MG6 4-6
WPF INF-MA ab 1
WF ICT-MA-MPS ab 1
WF CME-MA ab 1
WPF AI-MA ab 1
WPF ME-MA-MG6 1-3
- 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.
- 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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Computer Vision Exercise [CV-E] -
- Dozentinnen/Dozenten:
- Bernhard Egger, Shih-Yuan Huang, Sarma Jeet Sen, Maximilian Weiherer, Mathias Zinnen, Darius Rückert
- Angaben:
- Übung, 2 SWS, ECTS: 2,5, nur Fachstudium, Exercises are voluntary and will not be graded/corrected. The exam will contain questions on the excercises.
- Studienrichtungen / Studienfächer:
- WPF ME-BA-MG6 4-6
WPF INF-MA ab 1
WF ICT-MA-MPS ab 1
WPF AI-MA ab 1
WPF ME-BA-MG6 1-3
- Schlagwörter:
- computer vision; stereo vision; structure from motion; multi-view reconstruction; convolutional neural networks
| | | | Di | 10:00 - 12:00 | 0.01-142 CIP | |
Egger, B. | |
| | | Mi | 8:00 - 10:00 | 00.156-113 CIP | |
Egger, B. | |
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Datenbank Praxis [DBPraxis] -
- Dozent/in:
- Sebastian Wind
- Angaben:
- Vorlesung, 4 SWS, ECTS: 5, für FAU Scientia Gaststudierende zugelassen, Online-Kurs
- Termine:
- Online-Kurs
- Studienrichtungen / Studienfächer:
- WF INF-BA ab 4
WF INF-MA ab 1
WF INF-BA-V-SWE ab 4
WF INF-BA-V-DB ab 4
- 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, für FAU Scientia Gaststudierende zugelassen, Information regarding the online teaching will be added to the studon course
- Termine:
- Fr, 10:15 - 11:45, 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
PF ASC-MA 2
PF DS-MA ab 1
- Voraussetzungen / Organisatorisches:
- The following lectures are recommended:
https://www.studon.fau.de/crs4449450.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 for Beginners (VHB-Kurs) [DL4B] -
- Dozentinnen/Dozenten:
- Aline Sindel, Andreas Maier
- Angaben:
- Vorlesung, 2 SWS, ECTS: 2,5, für FAU Scientia Gaststudierende zugelassen
- Termine:
- Zeit/Ort n.V.
- Studienrichtungen / Studienfächer:
- WPF INF-BA ab 3
WPF MT-BA ab 3
WPF DS-BA ab 3
WF CE-BA-TW ab 3
- Voraussetzungen / Organisatorisches:
- Requirements: mathematics for engineering, basic knowledge of python
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:
- Neural networks have had an enormous impact on research in image and signal processing in recent years. In this course, you will learn all the basics about deep learning in order to understand how neural network systems are built. The course is addressed to students who are new to the field. In the beginning of the course, we introduce you to the topic with some applications of deep learning in the field of medical imaging, digital humanities and industry projects. Before we dive into the core elements of neural networks, there are two lecture units on the fundamentals of signal and image processing to teach you relevant parts of system theory such as convolutions, Fourier transform, and sampling theorem. In the next lecture units, you learn the basic blocks of neural networks, such as backpropagation, fully connected layers, convolutional layers, activation functions, loss functions, optimization, and regularization strategies. Then, we look into common practices for training and evaluating neural networks. The next lecture unit is focusing on common neural network architectures, such as LeNet, Alexnet, and VGG. It follows a lecture unit about unsupervised learning that contains the principles of autoencoders and generative adversarial networks. Lastly, we cover some applications of deep learning in segmentation and object detection.
The accompanying programming exercises will provide a deeper understanding of the workings and architecture of neural networks, in which you will develop a basic neural network from scratch in pure Python without using deep learning frameworks, such as PyTorch or TensorFlow.
At the end of the semester, there will be a written exam.
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Introduction to Machine Learning [IntroML] -
- Dozent/in:
- Vincent Christlein
- Angaben:
- Vorlesung, 2 SWS, Schein, ECTS: 3,75, für FAU Scientia Gaststudierende zugelassen, Information regarding the online teaching will be added to the studon course
- Termine:
- Fr, 8:30 - 10:00, H7
- Studienrichtungen / Studienfächer:
- WPF ME-BA-MG6 3-5
WPF MT-BA 5
WPF INF-BA-V-ME ab 5
WPF INF-BA-V-MI ab 5
WF CE-BA-TW ab 5
WPF INF-MA 1
WPF IuK-BA ab 5
WPF ME-MA-MG6 1-3
WPF DS-BA ab 2
- Voraussetzungen / Organisatorisches:
- https://www.studon.fau.de/crs4368398.html
- Schlagwörter:
- Mustererkennung, Vorverarbeitung, Merkmalsextraktion, Klassifikation
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Introduction to Machine Learning Exercises [IntroML-Ex] -
- Dozent/in:
- Paul Stöwer
- Angaben:
- Übung, 2 SWS, Schein, ECTS: 1,25, für FAU Scientia Gaststudierende zugelassen
- Studienrichtungen / Studienfächer:
- WPF ME-BA-MG6 3-5
WPF MT-BA 5
WPF INF-BA-V-ME ab 5
WPF INF-BA-V-MI ab 5
WF CE-BA-TW ab 5
WPF IuK-BA ab 5
WPF ME-MA-MG6 1-3
WPF DS-BA ab 2
- Schlagwörter:
- Mustererkennung, Vorverarbeitung, Merkmalsextraktion, Klassifkation
| | | | Mo | 8:15 - 9:45 | 00.152-113 | |
Stöwer, P. | |
| | | Di | 10:15 - 11:45 | 01.255-128 | |
Stöwer, P. | |
| | | Mi | 8:15 - 9:45 | 01.255-128 | |
Stöwer, P. | |
| | | Do | 10:15 - 11:45 | 01.255-128 | |
Stöwer, P. | |
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Kolloquium Magnetic Resonance Imaging [MRI] -
- Dozentinnen/Dozenten:
- Andreas Maier, Armin Nagel, Frederik Laun, Moritz Zaiß, Sebastian Bickelhaupt, Florian Knoll, Daniel Giese
- 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, für FAU Scientia Gaststudierende zugelassen
- 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, für FAU Scientia Gaststudierende zugelassen, 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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Medical Image Processing for Diagnostic Applications (VHB-Kurs) [MIPDA] -
- Dozentinnen/Dozenten:
- Andreas Maier, Luis Carlos Rivera Monroy, Celia Martín Vicario, Arpitha Ravi
- 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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Pattern Analysis [PA] -
- Dozent/in:
- Christian Riess
- Angaben:
- Vorlesung, 3 SWS, benoteter Schein, ECTS: 3,75, This course will be held as inverted classroom with physical meetings, with a "best-effort" online option.
- Termine:
- Do, 16:15 - 17:45, H16
Fr, 12:15 - 13:45, Zoom-Meeting
- 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.
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
- 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/crs4397541.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, Fabian Wagner, Paula Andrea Pérez-Toro
- Angaben:
- Praktikum, ECTS: 5, This course will be held online. No registration is required to attend this course. All further information will be provided in the StudOn course (link below).
- Termine:
- Do, 10:00 - 12:00, 0.01-142 CIP
- 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. The first lecture will take place 28 April at 10:00 am via Zoom.
- Schlagwörter:
- Master Project, Pattern Recognition, CT Reconstruction
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Seminar Advanced Deep Learning [SemADL] -
- Dozentinnen/Dozenten:
- Katharina Breininger, Vincent Christlein, Andreas Maier
- Angaben:
- Seminar, 2 SWS, ECTS: 5, nur Fachstudium
- Termine:
- Di, 12:00 - 14:00, Seminarraum ZMPT
The first session (April 26) of the semester will an online meeting, subsequent meetings will be in person.
- Studienrichtungen / Studienfächer:
- WPF INF-MA ab 1
WPF MT-MA-BDV ab 1
WPF CE-MA-TA-MT ab 1
- Inhalt:
- Deep Learning-based algorithms showed great performance in many fields of image processing and pattern recognition and compete with technologies such as compressive sensing and iterative optimization. The basis for the success of these algorithms is the availability of large amounts of data (big data) for training and of high computing power (typically GPUs).
In this seminar we try to explore advanced deep learning methods. In particular, we will aim to develop a deeper understanding of certain topics, for example: graph neural networks, unsupervised learning, differentiable learning, invertible learning, neural ordinary differential equations, transfer learning, multi-task learning, uncertainty DL, etc.
- Schlagwörter:
- algorithms; image processing
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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, für FAU Scientia Gaststudierende zugelassen
- 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] -
- Dozentinnen/Dozenten:
- Seung Hee Yang, Alexander Barnhill, Andreas Maier
- Angaben:
- Vorlesung, 2 SWS, benoteter Schein, ECTS: 5, nur Fachstudium
- Termine:
- Mo, 14:00 - 16:00, Hörsaal ZMPT
- 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/crs4464784.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:
- Seminar, ECTS: 2,5, nur Fachstudium
- Termine:
- Mo, 10:15 - 11:45, Übung 3 / 01.252-128
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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