By DKZ.2R: Introduction to Machine Learning with Python and Scikit-Learn

Date: Apr 1-2, 2026
Category: Workshop
Location: Aachen (RWTH Aachen University)
Workshop Python Machine Learning Carpentries

As part of our “Trainings” work package, the DKZ.2R creates, curates and presents a variety of free trainings, seminars and courses. Our next offering will be a one and a half day carpentries-style workshop on basic machine learning using python and scikit-learn, to be presented at RWTH Aachen University on 1st and 2nd of April, 2026. The workshop will cover the following topics:

  • What is Machine Learning / Why bother?
  • Supervised Methods (Regression / Classification)
  • Ensemble Methods
  • Unsupervised Methods (Clustering / Dimensionality Reduction)
  • Neural Networks
  • Ethics and Implications of Machine Learning

Workshop material is available online and will be presented by instructors who will walk you through the steps and are available for questions throughout the event. The official registration is already closed, for last-minute registrations please contact us via info@dkz2r.de.

All workshop material will also be made available online on GitHub.

A basic familiarity with Python is expected, including writing for loops, conditional statements, using functions, and importing libraries.

  • Title: Introduction to Machine Learning with Python and Scikit-Learn
  • When: Wednesday, April 1st (9am-5pm) & Thursday, April 2nd (9am-3pm)
  • Where: RWTH Aachen University, Germany (Kopernikusstraße 6, 52074 Aachen, Seminarraum 003)
  • Format: This workshop in In-Person and no remote or online attendance options are currently planned.
Tags :

Related Posts

By DKZ.2R: Introduction to Machine Learning with Python and Scikit-Learn

As part of our “Trainings” work package, the DKZ.2R creates, curates and presents a variety of free trainings, seminars and courses. Our next offering will be a one and a half day carpentries-style workshop on basic machine learning using python and scikit-learn, to be presented at RWTH Aachen University on 24th and 25th of July, 2025. The workshop will cover the following topics:

  • What is Machine Learning / Why bother?
  • Supervised Methods (Regression / Classification)
  • Ensemble Methods
  • Unsupervised Methods (Clustering / Dimensionality Reduction)
  • Neural Networks
  • Ethics and Implications of Machine Learning

Workshop material is available online and will be presented by instructors who will walk you through the steps and are available for questions throughout the event. If you are interested in taking part in the workshop, you can sign up here.

Read More

By DKZ.2R: The Carpentries Workshop: Introduction to the Unix Shell, Git, and GitLab

The DKZ.2R presents, as a part of our “Trainings” work package, a Carpentries Workshop on the 3rd and 7th of November on the topic of “Introduction to the Unix Shell, Git, and GitLab”. This is an official Carpentries Workshop and will be hosted on-site at RWTH Aachen University.

Workshop material is available online and will be presented by official Carpentries instructors, who will guide you through the concepts with the help of hands-on exercises and personalized support. The course is designed for beginners and is open to participants from all domains. No prior knowledge is required. If you are interested in taking part in the workshop, you can sign up here.

Read More
How To: Good Scientific Practice

How To: Good Scientific Practice

“Scientific integrity forms the basis for trustworthy research”, so it says in the Guidelines for Safeguarding Good Research Practice of the DFG, the German Research Foundation. As a major funder of research in Germany the DFG, as well as many other funders of research in Germany and the European Union, requires researchers to follow a certain set of rules conducting their research. These rules are called “good scientific practice” and have to be followed by researchers to be viable for funding. According to the guidelines researchers are required to “document all information relevant to the production of a research result as clearly as is required by and is appropriate for the relevant subject area to allow the result to be reviewed and assessed”. But good scientific practice is not done by documenting your research. It also includes i.a. protecting the personality rights of your subjects and handling research data in an appropriate manner by e.g. “back(-ing) up research data and results made publicly available, as well as the central materials on which they are based and the research software used, by adequate means according to the standards of the relevant subject area, and retain them for an appropriate period of time.” This is where Research Data Management (RDM) comes in. Of course RDM is much more than just creating a backup of your data on a USB-Stick and handing it over to anyone asking for it. “Good scientific practice” in RDM follows the FAIR principles:

Read More