Publications | April 2019

Einstieg ins Machine Learning – Grundlagen, Prinzipien, erste Schritte

In this ebook on the basics, principles and first steps into Machine Learning by Entwickler.de Magazine (in German), co-author Dr. Andreas Bühlmeier outlines the mathematical foundations for machine learning and showcases some of its most important and popular algorithms.
In German only

Media | September 2018

Entwickler Magazin Spezial Vol.17: Machine Learning

“Machine Learning: how the machines learn to learn” is the theme of this special edition of Entwickler magazine, to which Dr. Andreas Bühlmeier contributed with the opening feature, an in-depth mathematical look at “Die Wurzel des Machine Learnings” (lit. “the root of Machine Learning”.
In German only

Events | June 2018

The Conference for Machine Learning Innovation

At this conference running June 18-20, 2018 in Munich, Germany, Dr. Andreas Bühlmeier’s pragmatic session on June 19, “Kick-Start your Understanding of Machine Learning with Python”, focused on how to quickly build Machine Learning applications and understand what happens ‘under the hood’ using Python and by showcasing two examples: unsupervised and supervised learning for text classification.

Publications | May 2018

Kick-start Deep Learning mit TensorFlow und Keras

How does one quickly get into deep learning? With his kick-start guide, published in Entwickler.de magazine, Dr. Andreas Bühlmeier explains how to quickly set up an example with Python and the Keras package running. It’s not about taking into account various options and optimizing but about starting quickly – and then dive into the background. 
In German only (a non-literal translation in English is available in the BLOG section of this site.)



Academic Papers | 1994-98

While working as lecturer and researcher at the Universities of Bremen (alma mater, FB Informatik) and Dortmund, Dr. Andreas Bühlmeier published numerous scientific papers. His PhD thesis “Analog Neural Networks in Autonomous Systems” (1996) was based on and included extensive,  high empathy innovative research in the field.