phy611 - Theoretical Methods

phy611 - Theoretical Methods

Institute of Physics 6 KP
module responsibility
  • Simon Doclo
Prüfungsberechtigt
  • Jörn Anemüller
  • Kerstin Avila Canellas
  • Simon Doclo
  • Alexander Hartmann
  • Martin Kühn
  • Walter Neu
  • Björn Poppe
  • Bernhard Stoevesandt
  • Jann Strybny
  • Alexey Chernov
  • Christopher Gies
Module components Semester courses Wintersemester 2025/2026 Examination
Lecture
  • Unlimited access 5.04.4213 - Machine Learning I Show lecturers
    • Prof. Dr. Bernd Meyer

    Wednesday: 10:00 - 12:00, weekly (from 15/10/25)
    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
  • Unlimited access 5.04.4521 - Computerorientierte Physik Show lecturers
    • Prof. Dr. Alexander Hartmann

    Dates on Friday, 24.10.2025, Tuesday, 11.11.2025 14:00 - 16:00, Wednesday, 12.11.2025 14:00 - 18:00, Tuesday, 02.12.2025 14:00 - 16:00, Wednesday, 03.12.2025 14:00 - 18:00, Tuesday, 13.01.2026 14:00 - 16:00, Monday, 16.02.2026 13:00 - 14:00, Monday, 16.02.2026 14:00 - 20:00, Tuesday, 17.02.2026 13:00 - 14:00, Tuesday, 17.02.2026 14:00 - 20:00, Thursday, 19.02.2026 13:00 - 14:00 ...(more)
    Location: W02 1-143, 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
  • Unlimited access 5.04.4675 - Optical Simulation and Modelling (Zemax) Show lecturers
    • Prof. Dr. Walter Neu, Dipl.-Phys.

    The course times are not decided yet.
    Block lecture starting at the beginning of September. The related project must be finished at the beginning of December. Contact the lecturer Jim Napier <napier.w.jim@googlemail.com> for further details.
Exercises
  • Unlimited access 5.04.4213 Ü2 - Machine Learning I Show lecturers
    • Dmytro Velychko
    • Prof. Dr. Bernd Meyer
    • Jan Warnken

    Tuesday: 16:00 - 18:00, weekly (from 21/10/25), Location: W33 0-003, W02 1-148
  • Unlimited access 5.04.4213 Ü3 - Machine Learning I Show lecturers
    • Prof. Dr. Bernd Meyer
    • Jan Warnken

    Tuesday: 16:00 - 18:00, weekly (from 21/10/25)
  • Unlimited access 5.04.4521 - Computerorientierte Physik Show lecturers
    • Prof. Dr. Alexander Hartmann

    Dates on Friday, 24.10.2025, Tuesday, 11.11.2025 14:00 - 16:00, Wednesday, 12.11.2025 14:00 - 18:00, Tuesday, 02.12.2025 14:00 - 16:00, Wednesday, 03.12.2025 14:00 - 18:00, Tuesday, 13.01.2026 14:00 - 16:00, Monday, 16.02.2026 13:00 - 14:00, Monday, 16.02.2026 14:00 - 20:00, Tuesday, 17.02.2026 13:00 - 14:00, Tuesday, 17.02.2026 14:00 - 20:00, Thursday, 19.02.2026 13:00 - 14:00 ...(more)
    Location: W02 1-143, 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
  • Unlimited access 5.04.4675 - Optical Simulation and Modelling (Zemax) Show lecturers
    • Prof. Dr. Walter Neu, Dipl.-Phys.

    The course times are not decided yet.
    Block lecture starting at the beginning of September. The related project must be finished at the beginning of December. Contact the lecturer Jim Napier <napier.w.jim@googlemail.com> for further details.
Hinweise zum Modul
Prerequisites

basic programming skills (matlab, python, C/C++)
Module examination

According selected course
Skills to be acquired in this module

The goal of this module is to extend the training in theoretical methods for engineering physics through the acquisition of solid and in-depth knowledge of advanced concepts and through their practice with computer simulations. Depending on the chosen course, the students will have the opportunity to strengthen their knowledge in quantum material modelling (Density-functional theory), signal processing, fluid dynamics (Modelling and Simulation), computational physics, and machine learning. In this way, they will develop skills to relate the conceptual design of models, their numerical implementation, and the physical analysis of the produced data, with the results of field and/or laboratory measurements.

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