Lecture


5.04.4213  Machine Learning I  Probabilistic Unsupervised Learning
Wednesday: 10:15  11:45, weekly (from 20/10/21), Location: W32 0005 Dates on Tuesday, 08.03.2022  Friday, 11.03.2022 08:30  18:30, Monday, 14.03.2022 08:30  16:00, Tuesday, 15.03.2022 08:30  13:00, Monday, 30.05.2022 11:00  18:00, Tuesday, 31.05.2022 08:00  18:00, Location: W01 0012, W04 1171
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 largescale 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.unioldenburg.de/ml

5.04.4521  Computerorientierte Physik
 Prof. Dr. Alexander Hartmann
Dates on Tuesday, 19.10.2021, Tuesday, 02.11.2021 18:15  19:45, Thursday, 11.11.2021 16:15  19:45, Tuesday, 16.11.2021 18:15  19:45, Thursday, 25.11.2021 16:15  19:45, Tuesday, 11.01.2022 18:15  19:45, Monday, 21.02.2022 13:15  19:00, Monday, 21.02.2022 14:15  19:00, Tuesday, 22.02.2022 13:15  19:00, Tuesday, 22.02.2022 14:15  19:00, Wednesday, 23.02.2022 13:15  19:00, Wednesday, 23.02.2022 14:15  19:00, Thursday, 24.02.2022 13:15  19:00, Thursday, 24.02.2022 14:15  19:00, Friday, 25.02.2022 13:15  19:00, Friday, 25.02.2022 14:15  19:00, Monday, 28.02.2022 13:15  19:00, Monday, 28.02.2022 14:15  19:00, Tuesday, 01.03.2022 13:15  19:00, Tuesday, 01.03.2022 14:15  19:00, Wednesday, 02.03.2022 13:15  19:00, Wednesday, 02.03.2022 14:15  19:00, Thursday, 03.03.2022 13:15  19:00, Thursday, 03.03.2022 14:15  19:00, Friday, 04.03.2022 13:15  19:00, Friday, 04.03.2022 14:15  19:00 ...(more) Location: W02 1148, W01 0008 (Rechnerraum), W01 0006
Debugging, Datenstrukturen, Algorithmen, Zufallszahlen, Daten analyse, Perkolation, MonteCarloSimulationen, FiniteSize Scaling, QuantenMonteCarlo, MolekulardynamikSimulationen, ereignisgetriebene Simulationen, Graphen und Algorithmen, genetische Algorithmen, Optimierungsprobleme

5.04.4571  Densityfunctional theory
 Prof. Dr. Caterina Cocchi
Tuesday: 14:15  15:45, weekly (from 19/10/21) Thursday: 10:15  11:45, weekly (from 21/10/21)

5.04.4665  Modelling and Simulation
Monday: 09:45  13:00, weekly (from 25/10/21)
Contact: jann.strybny@hsemdenleer.de
arne.daniel@hsemdenleer.de
• Understanding of advanced fluid dynamics including threedimensional, 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 OpenSourceCFDTools, considering preprocessing, processing and postprocessing tools
• Need and availability of appropriate measurement techniques for the steering, calibration and verification of models
• Contactless highresolving measuring techniques in the fluid dynamics
• Limits of accuracy of different modelling and simulation concepts

5.04.4675  Optical Simulation and Modelling (Zemax)
 Prof. Dr. Walter Neu, Dipl.Phys.
Monday: 17:00  19:00, weekly (from 18/10/21)
lecture and project


Exercises


5.04.4213  Machine Learning I  Probabilistic Unsupervised Learning
Wednesday: 10:15  11:45, weekly (from 20/10/21), Location: W32 0005 Dates on Tuesday, 08.03.2022  Friday, 11.03.2022 08:30  18:30, Monday, 14.03.2022 08:30  16:00, Tuesday, 15.03.2022 08:30  13:00, Monday, 30.05.2022 11:00  18:00, Tuesday, 31.05.2022 08:00  18:00, Location: W01 0012, W04 1171
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 largescale 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.unioldenburg.de/ml

5.04.4213 Ü1  Machine Learning I  Probabilistic Unsupervised Learning
 Prof. Dr. Jörg Lücke
 Florian Hirschberger
 Filippos Panagiotou
Tuesday: 16:15  17:45, weekly (from 26/10/21), Location: W32 0005, W32 1112

5.04.4213 Ü2  Machine Learning I  Probabilistic Unsupervised Learning
 Prof. Dr. Jörg Lücke
 Filippos Panagiotou
 Florian Hirschberger
Tuesday: 16:15  17:45, weekly (from 26/10/21)

5.04.4213 Ü3  Machine Learning I  Probabilistic Unsupervised Learning
 Prof. Dr. Jörg Lücke
 Dmytro Velychko
Tuesday: 10:15  11:45, weekly (from 26/10/21), Location: W04 1172, W01 0012

5.04.4665  Modelling and Simulation
Monday: 09:45  13:00, weekly (from 25/10/21)
Contact: jann.strybny@hsemdenleer.de
arne.daniel@hsemdenleer.de
• Understanding of advanced fluid dynamics including threedimensional, 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 OpenSourceCFDTools, considering preprocessing, processing and postprocessing tools
• Need and availability of appropriate measurement techniques for the steering, calibration and verification of models
• Contactless highresolving measuring techniques in the fluid dynamics
• Limits of accuracy of different modelling and simulation concepts

5.04.4675  Optical Simulation and Modelling (Zemax)
 Prof. Dr. Walter Neu, Dipl.Phys.
Monday: 17:00  19:00, weekly (from 18/10/21)
lecture and project

