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
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.
This module aims to broaden the training of the students in theoretical methods for engineering physics by providing a solid and in-depth understanding of advanced concepts and their application through computer simulations. Depending on the course chosen, the students may strengthen their knowledge of computer physics, machine learning, signal processing, modelling and simulation, or numerical methods for differential equations. The module enables the students to develop the skills needed to link model design, numerical implementation, and physical data analysis with the interpretation of field and/or laboratory measurements.