phy611 - Theoretical Methods (Course overview)

phy611 - Theoretical Methods (Course overview)

Institute of Physics 6 KP
Module components Semester courses Winter semester 2024/2025 Examination
Lecture
  • No access 5.04.4213 - Machine Learning I - Probabilistic Unsupervised Learning Show lecturers
    • Prof. Dr. Jörg Lücke

    Wednesday: 10:00 - 12:00, weekly (from 16/10/24), Location: A10 1-121 (Hörsaal F)
    Dates on Tuesday, 18.02.2025 15:30 - 17:30, Location: W02 1-148
    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
  • No access 5.04.4521 - Computerorientierte Physik Show lecturers
    • Prof. Dr. Alexander Hartmann

    Dates on Thursday, 24.10.2024, Thursday, 07.11.2024 12:00 - 14:00, Monday, 18.11.2024 16:00 - 20:00, Thursday, 05.12.2024 12:00 - 14:00, Tuesday, 10.12.2024 16:00 - 20:00, Monday, 17.02.2025 13:00 - 14:00, Monday, 17.02.2025 14:00 - 18:00, Tuesday, 18.02.2025 13:00 - 14:00, Tuesday, 18.02.2025 14:00 - 18:00, Wednesday, 19.02.2025 13:00 - 14:00, Wednesday, 19.02.2025 14:00 - 18:00 ...(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
  • No access 5.04.4571 - Density-functional theory Show lecturers
    • Dr. Ana Maria Valencia Garcia

    Wednesday: 14:00 - 16:00, weekly (from 16/10/24)
    Friday: 08:00 - 10:00, weekly (from 18/10/24)
    Description: The objective of this class is to introduce students to ab initio methods for electronic-structure calculations based on density-functional theory (DFT). The topics will be approached from the viewpoint of condensed-matter physics. In the first part of the semester, theoretical lectures will be accompanied by exercise sessions. The last few weeks of the term will be exclusively dedicated to hand-on tutorials. At the end of this course, participants are expected to be familiar with the theoretical foundation of DFT, to be able to perform a DFT calculation with good control of the given approximations, and to know how to interpret the outcoming results. The course as a whole (theoretical lectures, exercises, and hands-on tutorials) can be offered in presence, in digital form, or even in a mixed regime, depending on the circumstances and on the students’ needs. The course is addressed to Master’s students in theoretical physics. However, Master’s students in experimental physics, doctoral students in all specializations, and Bachelor’s students who are interested in (computational) electronic-structure theory are very welcome to participate. Good knowledge of quantum mechanics is the only prerequisite to attend this class.
Exercises
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.