Machine Learning

Machine Learning is a central subfield of artificial intelligence. Many digital applications in our daily lives use machine learning: spam filters sort out unwanted emails, streaming services recommend suitable content, and voice assistants respond to spoken requests. In all these cases, the systems use large amounts of data to recognise patterns and derive decisions from them.

Discover now on the AI Campus how different algorithms and learning methods work, what they are suitable for, and how you can use them in a targeted way.

Selected courses

Course

Introduction to Machine Learning Part 3: Evaluation and Tuning

Munich Center for Machine Learning
⏱ 40 hours
🏅︎ Record of Achievement
★ 4 (3)
🌐︎ English
AI Campus Original Machine Learning Data Science & Big Data AI in Higher Education
Course

Holistic applied AI in engineering – Deep learning for sequential process data

TU Dresden
⏱ 21 hours
🏅︎ Record of Achievement
🌐︎ German
AI Campus Original Human-Computer Interaction Machine Learning AI in Companies
Course

Holistic applied AI in engineering – process informatics

TU Dresden
⏱ 21 hours
🏅︎ Record of Achievement
🌐︎ German
AI Campus Original Machine Learning Human-Computer Interaction AI in Companies

Experts

Matthieu Deru
Dr. Matthieu Deru
Deutsches Forschungszentrum für Künstliche Intelligenz
Gabriela_Molinar
Dr.-Ing. Gabriela Molinar
KIT
Alassane Ndiaye
Dr.-Ing. Alassane Ndiaye
Deutsches Forschungszentrum für Künstliche Intelligenz
Stephan Scheele
Dr. Stephan Scheele
Fraunhofer IIS
Prof.Dr. Marco Huber
Prof. Dr. Marco Huber
Universität Stuttgart Fraunhofer IPA

Prof. Marco Huber, born on 12 January 1980 in Kehl, received his doctorate in computer science from the University of Karlsruhe (TH) in 2009. From 2009 to 2011, he headed the research group "Variable Image Acquisition and Processing" at the Fraunhofer Institute of Optronics, System Technologies and Image Exploitation IOSB in Karlsruhe. He then worked as a senior researcher at AGT International in Darmstadt until 2015. From April 2015 until September 2018, Prof. Huber was responsible for product development and data science services in the Katana division at USU Software AG in Karlsruhe. At the same time, he taught as a private lecturer in computer science at the Karlsruhe Institute of Technology (KIT). Since October 2018, he has held the professorship for cognitive production systems at the University of Stuttgart and is also head of the Centre for Cyber-Cognitive Intelligence (CCI) at the Fraunhofer Institute for Manufacturing Engineering and Automation IPA. His research focuses on the topics of machine learning, sensor data analysis and robotics in the production engineering environment.

Topic

Machine Learning
Gabriela_Molinar
Dr.-Ing. Gabriela Molinar
KIT

Gabriela Molinar studied electrical engineering and information technology in Venezuela. She received her doctorate in 2020 from the Institute for Information Processing Technology (ITIV) at the Karlsruhe Institute of Technology (KIT) and now works in the energy sector at the transmission system operator TenneT TSO GmbH in Bayreuth.

She received the 2022 Ecology Award of the Viktor & Sigrid Dulger Foundation for her work "Machine Learning Tool for Transmission Capacity Forecasting of Overhead Lines based on Distributed Weather Data", which makes an important contribution to power load forecasting using artificial intelligence as an optimisation method for grid operation and to support the energy transition in Germany.

Subscribe to Machine Learning

Helpdesk

Hint: Have you already checked our FAQ for an answer to your question?
CAPTCHA
Image CAPTCHA
Enter the characters shown in the image.
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.