Vorlesung: 5.04.4213 Machine Learning I - Probabilistic Unsupervised Learning - Details

Vorlesung: 5.04.4213 Machine Learning I - Probabilistic Unsupervised Learning - Details

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Veranstaltungsname Vorlesung: 5.04.4213 Machine Learning I - Probabilistic Unsupervised Learning
Untertitel
Veranstaltungsnummer 5.04.4213
Semester WiSe20/21
Aktuelle Anzahl der Teilnehmenden 41
erwartete Teilnehmendenanzahl 20
Heimat-Einrichtung Institut für Physik
Veranstaltungstyp Vorlesung in der Kategorie Lehre
Erster Termin Mittwoch, 21.10.2020 10:00 - 12:00, Ort: (online)
Art/Form V, Ü
Voraussetzungen Basic knowledge in higher Mathematics as taught as part of first degrees in Physics, Mathematics, Statistics, Engineering or Computer Science (basic linear algebra and analysis). Basic programming skills (course supports matlab & python). Many relations to statistical physics, statistics, probability theory, stochastic but the course's content will be developed independently of detailed prior knowledge in these fields.
Leistungsnachweis Exam or presentation or oral exam or homework or practical report
Lehrsprache englisch
Info-Link http://www.uni-oldenburg.de/fileadmin/user_upload/physik/PDF/Modulhandbuecher/Modulhandbuch_Fach-Master_Physik_2015_WS.pdf#page=81
ECTS-Punkte 6

Räume und Zeiten

(online)
Mittwoch: 10:00 - 12:00, wöchentlich (14x)
W01 0-015
Dienstag, 23.02.2021 - Freitag, 26.02.2021 09:00 - 18:30

Modulzuordnungen

Kommentar/Beschreibung

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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