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Einrichtungen >> Technische Fakultät (TF) >> Verwaltung und Serviceeinrichtungen Technische Fakultät >> MAOT - Master Programme in Advanced Optical Technologies (Elitestudiengang) >>
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Geschäftsstelle MAOT
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Advanced Laser -
- Dozent/in:
- Nicolas Joly
- Angaben:
- Vorlesung mit Übung, 4 SWS, Schein, ECTS: 5
- Termine:
- Fr, 12:30 - 16:30, Zoom-Meeting
- Studienrichtungen / Studienfächer:
- WPF AOT-GL 2-3
- Voraussetzungen / Organisatorisches:
- Due to the corona virus situation the courses will be conducted as an e-learning course. Please go to the StudOn-link provided below for more information.
- Inhalt:
- Z-cavity
Dispersion management for ultra-short pulse generation Various technique of characterisation of ultra-short pulses Polarisation effects and Jones’ formalism Semi-classical model for a laser (Maxwell-Bloch equations)
The rest of the lecture will consist of seminar presented by the students on the topics of their choice. These topics should cover a particular aspect (fundamental, theoretical, applied) of a laser system or an application of laser (e.g. optical tweezer, high-precision metrology, high-resolution spectroscopy… etc)
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Advanced nonlinear optics -
- Dozentinnen/Dozenten:
- Nicolas Joly, Maria Chekhova, Hanieh Fattahi
- Angaben:
- Vorlesung, 4 SWS, ECTS: 5, nur Fachstudium
- Termine:
- Do, 10:30 - 12:30, Zoom-Meeting
via Zoom (online lecture)
- Studienrichtungen / Studienfächer:
- WPF Ph-MA 1
WPF AOT-GL 1
- Voraussetzungen / Organisatorisches:
- The course will be conducted as online course. For more details and registration please go to
https://www.studon.fau.de/crs3671904_join.html
- Inhalt:
- The goal of this lecture is to explore advanced concepts of nonlinear optics and their applications. This will cover the following topics:
Nonlinear propagation in solid-core photonic crystal fibres (modulation instability, four-wave mixing, soliton dynamics, supercontinuum generation) and in hollow-core photonic crystal fibres (generation of tunable dispersive waves, plasma in fibres)
Nonlinear optical effects (parametric down-conversion, four-wave mixing, modulation instability) for the generation of nonclassical light (entangled photons, squeezed light, twin beams, heralded single photons).
Nonlinear effects for generating high energy sub cycle pulses (kerr-lens mode-locking, Yb:YAG laser technology, optical parametric amplification, pulses synthesis, attosecond pulse generation)
- Schlagwörter:
- Please register using StudOn (StudOn-ID: https://www.studon.fau.de/crs3671904_join.html)
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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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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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Light Scattering: Lecture [OM/LS] -
- Dozentinnen/Dozenten:
- Andreas Paul Fröba, Michael Rausch, und Mitarbeiter/innen
- Angaben:
- Vorlesung, 2 SWS, ECTS: 5, This lecture course is offered online via ZOOM at the times stated in UnivIS as long as on-site attendence is not possible due to the Corona pandemic. First lecture is on Monday, April 12, 2021 at 06:15 p.m. For attending the lectures and exercises, registration for the StudOn-course "Light Scattering" until Friday, April 09, 2021 at 12:00 a.m. is mandatory (https://www.studon.fau.de/crs2182923.html). Registration will be open from April 01, 2021.
- Termine:
- Mo, 18:15 - 19:45, AOT-Kursraum
- Studienrichtungen / Studienfächer:
- WPF AOT-GL 2-3
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Machine Learning for Physicists -
- Dozent/in:
- Florian Marquardt
- Angaben:
- Vorlesung, 2 SWS, ECTS: 5, nur Fachstudium, die Vorlesung wird aufgrund der aktuellen Situation als "inverted classroom" angeboten, siehe zusätzliche Informationen - Due to the current situation, this lecture is moved to an "inverted classroom" format; see additional information; registration required: please follow zoom registration link on https://machine-learning-for-physicists.org
- Termine:
- Mo, 18:00 - 20:00, Raum n.V.
- Studienrichtungen / Studienfächer:
- WF Ph-BA ab 5
WF Ph-MA ab 1
WF PhM-BA ab 5
WF PhM-MA ab 1
- Voraussetzungen / Organisatorisches:
- This is a course introducing modern techniques of machine learning, especially deep neural networks, to an audience of physicists. Neural networks can be trained to perform diverse challenging tasks, including image recognition and natural language processing, just by training them on many examples. Neural networks have recently achieved spectacular successes, with their performance often surpassing humans. They are now also being considered more and more for applications in physics, ranging from predictions of material properties to analyzing phase transitions. We will cover the basics of neural networks, convolutional networks, autoencoders, restricted Boltzmann machines, and recurrent neural networks, as well as the recently emerging applications in physics. Prerequisites: almost none, except for matrix multiplication and the chain rule.
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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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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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Praktikum in Thermophysikalische Eigenschaften von Arbeitsstoffen der Verfahrens- und Energietechnik [TPE-PR] -
- Dozentinnen/Dozenten:
- Thomas Koller, Michael Rausch, Andreas Paul Fröba, Tobias Klein
- Angaben:
- Praktikum, 3 SWS, Schein, ECTS: 2,5, The lab course is offered online via ZOOM. Registration is possible within the first lecture in "Vorlesung und Übung in Thermophysikalische Eigenschaften von Arbeitsstoffen der Verfahrens- und Energietechnik".
- Termine:
- Do, 16:15 - 18:30, AOT-Kursraum
- Studienrichtungen / Studienfächer:
- WPF CBI-MA ab 1
WPF CEN-MA ab 1
WF LSE-MA ab 1
WPF ET-MA-VTE ab 1
- Voraussetzungen / Organisatorisches:
- Vorlesung und Übung in Thermophysikalische Eigenschaften von Arbeitsstoffen der Verfahrens- und Energietechnik
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Thermophysikalische Eigenschaften von Arbeitsstoffen der Verfahrens- und Energietechnik [TPE] -
- Dozentinnen/Dozenten:
- Thomas Koller, Andreas Paul Fröba, Michael Rausch, Tobias Klein
- Angaben:
- Vorlesung mit Übung, 4 SWS, ECTS: 5, The course is given online via ZOOM in English at the given times as long as on-site attendance is not possible due to the Corona pandemic. The first lecture will be on April 13, 2021 at 08:15 a.m. For attending the lecture, registration for the StudOn-course "Thermophysical Properties / Thermophysikalische Eigenschaften" until April 09, 2021 at 12:00 a.m. is mandatory (link: https://www.studon.fau.de/crs1525524.html). An optional lab course is offered in context with this lecture.
- Termine:
- Di, Mi, 8:15 - 9:45, AOT-Kursraum
- Studienrichtungen / Studienfächer:
- WF AOT-GL ab 1
WPF CBI-MA ab 1
WPF CEN-MA ab 1
WF LSE-MA ab 1
WPF ET-MA-VTE ab 1
WF MAP-SOFT ab 1
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