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Event

Semester: Winter term 2017

5.04.4213 Machine Learning I - Probabilistic Unsupervised Learning -  


Event date(s) | room

  • Dienstag, 17.10.2017 16:00 - 18:00
  • Mittwoch, 18.10.2017 10:00 - 12:00 | W01 0-006
  • Dienstag, 24.10.2017 16:00 - 18:00 | W02 1-148
  • Mittwoch, 25.10.2017 10:00 - 12:00 | W01 0-006
  • Mittwoch, 1.11.2017 10:00 - 12:00 | W01 0-006
  • Dienstag, 7.11.2017 16:00 - 18:00 | W02 1-148
  • Mittwoch, 8.11.2017 10:00 - 12:00 | W01 0-006
  • Dienstag, 14.11.2017 16:00 - 18:00 | W02 1-148
  • Mittwoch, 15.11.2017 10:00 - 12:00 | W01 0-006
  • Dienstag, 21.11.2017 16:00 - 18:00 | W02 1-148
  • Mittwoch, 22.11.2017 10:00 - 12:00 | W01 0-006
  • Dienstag, 28.11.2017 16:00 - 18:00 | W02 1-148
  • Mittwoch, 29.11.2017 10:00 - 12:00 | W01 0-006
  • Dienstag, 5.12.2017 16:00 - 18:00
  • Mittwoch, 6.12.2017 10:00 - 12:00 | W01 0-006
  • Dienstag, 12.12.2017 16:00 - 18:00
  • Mittwoch, 13.12.2017 10:00 - 12:00 | W01 0-006
  • Dienstag, 19.12.2017 16:00 - 18:00 | W02 1-148
  • Mittwoch, 20.12.2017 10:00 - 12:00 | W01 0-006
  • Dienstag, 9.1.2018 16:00 - 18:00 | W02 1-148
  • Mittwoch, 10.1.2018 10:00 - 12:00 | W01 0-006
  • Dienstag, 16.1.2018 16:00 - 18:00 | W02 1-148
  • Mittwoch, 17.1.2018 10:00 - 12:00 | W01 0-006
  • Dienstag, 23.1.2018 16:00 - 18:00 | W02 1-148
  • Mittwoch, 24.1.2018 10:00 - 12:00 | W01 0-006
  • Dienstag, 30.1.2018 16:00 - 18:00 | W02 1-148
  • Mittwoch, 31.1.2018 10:00 - 12:00 | W01 0-006

Description

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

lecturer

Tutor

Study fields

  • Studium generale / Gasthörstudium

Lehrsprache
englisch

Für Gasthörende / Studium generale geöffnet:
Ja

Info-Link
http://www.uni-oldenburg.de/fileadmin/user_upload/physik/PDF/Modulhandbuecher/Modulhandbuch_Fach-Master_Physik_2015_WS.pdf#page=81

(Changed: 19 Jan 2024)  | 
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