Events, media and publications by, with and about Dr. Andreas Bühlmeier and DBC Enterprise IT Consulting.
01/ 2023 New contracts on Cyber Security, SIEM, Splunk and Data Science with Python on Cloud environments with an even greater team
11 and 12 / 2022 Successfully providing training courses about Splunk (‘… exactly what I was looking for…’)
10/2022 Welcoming new Team Member for Splunk and Python
08/2022 Offizieller Praxispartner ‘IU Duales Studium’
06/ 2022 Speaker at Machine Learning Conference in Munich, June 2022: How Machine Learning is (becoming) creative
In 2021, contracts in fraud protection with Machine Learning continued strongly, as well as for predictive maintenance. Additionally training courses were provided for cyber security (Splunk) and Apache Spark/GraphX.
Hope to see you at ML 2021, Berlin https://mlconference.ai/speaker/dr-andreas-buhlmeier/:
ADMIN | Network & Security Magazine
In the upcoming 02/2021 issue of ADMIN | Network & Security magazine, a bi-monthly technical journal for system administrators, first-published 2010, Dr. Andreas Bühlmeier article “Computer Cop: Machine Learning and Security” addresses the use of Machine Learning for risk mitigation and defence from cybercrime.
In English only
Events | December 2020
At the IT-Tage 2020 (lit. “IT days 2020”) remote conference for Software Development, Architecture, Database and Management on December 7-10, typically hosted in Frankfurt am Main, Germany, Dr. Andreas Bühlmeier‘s morning session on December was held in English and covered how cybersecurity currently uses Machine Learning (ML) approaches, hands-on examples, risk mitigation from cyberattacks, and a future outlook.
Media | December 2020
IT infrastructure cybercrime is one of the greatest risks for private and public companies, institutions and organizations. In the article “Machine Learning und Security”, Dr. Andreas Bühlmeier explores the growing role of Machine Learning in the defense against criminal attacks on IT infrastructure.
In German only
Events | December 2019
The Conference for Machine Learning Innovation 2019
At the ML conference on December 9-11 in Berlin, Germany, Dr. Andreas Bühlmeier’s session on December 11, All You Need to Know Now about (deep) Reinforcement Learning, offered a pragmatic 45 minute primer on the foundations of reinforcement learning and how it evolved to current successes. The session included live demos with insights on Q-Learning principles and an outlook into future applications in specific industry areas such as pharmacy and logistics.
“I really like the highly structured presentation and the way the speaker provided a broad overview of reinforcement learning. Exactly what I wanted to hear 🙂 One of my favorite talks of the day!”
Events | November 2019
The Big Data Conference Europe 2019
At this three-day technical conference focused on the fields of Big Data, High Load, Data Science, Machine Learning and AI, Dr. Bühlmeier’s session on November 27, 2019 focused on the Breakthroughs and Future of Deep Reinforcement Learning.
The session was highly appreciated and scored an officially rating of “High” by 80 out of the 101 attendees.
Links to the full presentation (in English) are available here for video and slides.
Media | October 2019
Enwickler Magazin Spezial Vol. 22: Architektur
A revised version for print of Dr. Andreas Bühlmeier article on how to quickly get started with deep learning using Keras, Tensor Flow and a strategy of not taking into account various options and optimizing – rather start quickly and then dive into the background. Previously published online at Enwickler.de, the article is now also available in the latest issue of the print magazine Entwickler Spezial, focused on Architecture – Methods and Solutions for Software Development.
In German only (a non-literal translation in English of the original online publication is available in the BLOG section of this site.)
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 article, an in-depth mathematical look at the root of Machine Learning.
In German only
Events | June 2018
The Conference for Machine Learning Innovation 2018
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, the German online IT media channel associated with Entwickler magazine that publishes IT news and information on tools, technologies and techniques for software developers, 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.