Vorlesung
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5.04.4213 - Machine Learning I - Probabilistic Unsupervised Learning
Mittwoch: 10:15 - 11:45, wöchentlich (ab 20.10.2021), Ort: W32 0-005 Termine am Dienstag, 08.03.2022 - Freitag, 11.03.2022 08:30 - 18:30, Montag, 14.03.2022 08:30 - 16:00, Dienstag, 15.03.2022 08:30 - 13:00, Montag, 30.05.2022 11:00 - 18:00, Dienstag, 31.05.2022 08:00 - 18:00, Ort: W01 0-012, W04 1-171
The field of Machine Learning develops and provides methods for the analysis of data and signals. Typical application domains are computer hearing, computer vision, general pattern recognition and large-scale data analysis (recently often termed "Big Data"). Furthermore, Machine Learning methods serve as models for information processing and learning in humans and animals, and are often considered as part of artificial intelligence approaches.
This course gives an introduction to unsupervised learning methods, i.e., methods that extract knowledge from data without the requirement of explicit knowledge about individual data points. We will introduce a common probabilistic framework for learning and a methodology to derive learning algorithms for different types of tasks. Examples that are derived are algorithms for clustering, classification, component extraction, feature learning, blind source separation and dimensionality reduction. Relations to neural network models and learning in biological systems will be discussed were appropriate.
The course requires some programming skills, preferably in Matlab or Python. Further requirements are typical mathematical / analytical skills that are taught as part of Bachelor degrees in Physics, Mathematics, Statistics, Computer and Engineering Sciences. Course assignments will include analytical tasks and programming task which can be worked out in small groups.
The presented approach to unsupervised learning relies on Bayes' theorem and is therefore sometimes referred to as a Bayesian approach. It has many interesting relations to physics (e.g., statistical physics), statistics and mathematics (analysis, probability theory, stochastic) but the course's content will be developed independently of detailed prior knowledge in these fields.
Weblink: www.uni-oldenburg.de/ml
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5.04.4521 - Computerorientierte Physik
- Prof. Dr. Alexander Hartmann
Termine am Dienstag, 19.10.2021, Dienstag, 02.11.2021 18:15 - 19:45, Donnerstag, 11.11.2021 16:15 - 19:45, Dienstag, 16.11.2021 18:15 - 19:45, Donnerstag, 25.11.2021 16:15 - 19:45, Dienstag, 11.01.2022 18:15 - 19:45, Montag, 21.02.2022 13:15 - 19:00, Montag, 21.02.2022 14:15 - 19:00, Dienstag, 22.02.2022 13:15 - 19:00, Dienstag, 22.02.2022 14:15 - 19:00, Mittwoch, 23.02.2022 13:15 - 19:00, Mittwoch, 23.02.2022 14:15 - 19:00, Donnerstag, 24.02.2022 13:15 - 19:00, Donnerstag, 24.02.2022 14:15 - 19:00, Freitag, 25.02.2022 13:15 - 19:00, Freitag, 25.02.2022 14:15 - 19:00, Montag, 28.02.2022 13:15 - 19:00, Montag, 28.02.2022 14:15 - 19:00, Dienstag, 01.03.2022 13:15 - 19:00, Dienstag, 01.03.2022 14:15 - 19:00, Mittwoch, 02.03.2022 13:15 - 19:00, Mittwoch, 02.03.2022 14:15 - 19:00, Donnerstag, 03.03.2022 13:15 - 19:00, Donnerstag, 03.03.2022 14:15 - 19:00, Freitag, 04.03.2022 13:15 - 19:00, Freitag, 04.03.2022 14:15 - 19:00 ...(mehr) Ort: W02 1-148, W01 0-008 (Rechnerraum), W01 0-006
Debugging, Datenstrukturen, Algorithmen, Zufallszahlen, Daten- analyse, Perkolation, Monte-Carlo-Simulationen, Finite-Size Scaling, Quanten-Monte-Carlo, Molekulardynamik-Simulationen, ereignisgetriebene Simulationen, Graphen und Algorithmen, genetische Algorithmen, Optimierungsprobleme
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5.04.4571 - Density-functional theory
- Prof. Dr. Caterina Cocchi
Dienstag: 14:15 - 15:45, wöchentlich (ab 19.10.2021) Donnerstag: 10:15 - 11:45, wöchentlich (ab 21.10.2021)
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5.04.4665 - Modelling and Simulation
Montag: 09:45 - 13:00, wöchentlich (ab 25.10.2021)
Contact: jann.strybny@hs-emden-leer.de
arne.daniel@hs-emden-leer.de
• Understanding of advanced fluid dynamics including three-dimensional, transient and compressible processes
• Identifying the significant physical processes, defining the dimensionality and relevant scales in time and space
• Theory of similarity, range of dimensionless numbers
• Potential Theory
• Numerical Algorithms and possibilities of independent coding of simplest mathematical models
• Limitations of numerical models, risk of empirical approaches included in numerical models
• Introduction of a complete chain of Open-Source-CFD-Tools, considering preprocessing, processing and postprocessing tools
• Need and availability of appropriate measurement techniques for the steering, calibration and verification of models
• Contactless high-resolving measuring techniques in the fluid dynamics
• Limits of accuracy of different modelling and simulation concepts
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5.04.4675 - Optical Simulation and Modelling (Zemax)
- Prof. Dr. Walter Neu, Dipl.-Phys.
Montag: 17:00 - 19:00, wöchentlich (ab 18.10.2021)
lecture and project
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Übung
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5.04.4213 - Machine Learning I - Probabilistic Unsupervised Learning
Mittwoch: 10:15 - 11:45, wöchentlich (ab 20.10.2021), Ort: W32 0-005 Termine am Dienstag, 08.03.2022 - Freitag, 11.03.2022 08:30 - 18:30, Montag, 14.03.2022 08:30 - 16:00, Dienstag, 15.03.2022 08:30 - 13:00, Montag, 30.05.2022 11:00 - 18:00, Dienstag, 31.05.2022 08:00 - 18:00, Ort: W01 0-012, W04 1-171
The field of Machine Learning develops and provides methods for the analysis of data and signals. Typical application domains are computer hearing, computer vision, general pattern recognition and large-scale data analysis (recently often termed "Big Data"). Furthermore, Machine Learning methods serve as models for information processing and learning in humans and animals, and are often considered as part of artificial intelligence approaches.
This course gives an introduction to unsupervised learning methods, i.e., methods that extract knowledge from data without the requirement of explicit knowledge about individual data points. We will introduce a common probabilistic framework for learning and a methodology to derive learning algorithms for different types of tasks. Examples that are derived are algorithms for clustering, classification, component extraction, feature learning, blind source separation and dimensionality reduction. Relations to neural network models and learning in biological systems will be discussed were appropriate.
The course requires some programming skills, preferably in Matlab or Python. Further requirements are typical mathematical / analytical skills that are taught as part of Bachelor degrees in Physics, Mathematics, Statistics, Computer and Engineering Sciences. Course assignments will include analytical tasks and programming task which can be worked out in small groups.
The presented approach to unsupervised learning relies on Bayes' theorem and is therefore sometimes referred to as a Bayesian approach. It has many interesting relations to physics (e.g., statistical physics), statistics and mathematics (analysis, probability theory, stochastic) but the course's content will be developed independently of detailed prior knowledge in these fields.
Weblink: www.uni-oldenburg.de/ml
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5.04.4213 Ü1 - Machine Learning I - Probabilistic Unsupervised Learning
- Prof. Dr. Jörg Lücke
- Florian Hirschberger
- Filippos Panagiotou
Dienstag: 16:15 - 17:45, wöchentlich (ab 26.10.2021), Ort: W32 0-005, W32 1-112
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5.04.4213 Ü2 - Machine Learning I - Probabilistic Unsupervised Learning
- Prof. Dr. Jörg Lücke
- Filippos Panagiotou
- Florian Hirschberger
Dienstag: 16:15 - 17:45, wöchentlich (ab 26.10.2021)
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5.04.4213 Ü3 - Machine Learning I - Probabilistic Unsupervised Learning
- Prof. Dr. Jörg Lücke
- Dmytro Velychko
Dienstag: 10:15 - 11:45, wöchentlich (ab 26.10.2021), Ort: W04 1-172, W01 0-012
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5.04.4665 - Modelling and Simulation
Montag: 09:45 - 13:00, wöchentlich (ab 25.10.2021)
Contact: jann.strybny@hs-emden-leer.de
arne.daniel@hs-emden-leer.de
• Understanding of advanced fluid dynamics including three-dimensional, transient and compressible processes
• Identifying the significant physical processes, defining the dimensionality and relevant scales in time and space
• Theory of similarity, range of dimensionless numbers
• Potential Theory
• Numerical Algorithms and possibilities of independent coding of simplest mathematical models
• Limitations of numerical models, risk of empirical approaches included in numerical models
• Introduction of a complete chain of Open-Source-CFD-Tools, considering preprocessing, processing and postprocessing tools
• Need and availability of appropriate measurement techniques for the steering, calibration and verification of models
• Contactless high-resolving measuring techniques in the fluid dynamics
• Limits of accuracy of different modelling and simulation concepts
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5.04.4675 - Optical Simulation and Modelling (Zemax)
- Prof. Dr. Walter Neu, Dipl.-Phys.
Montag: 17:00 - 19:00, wöchentlich (ab 18.10.2021)
lecture and project
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