Machine Learning Summer School
Why Machine Learning?
Machine Learning is enabling a transformation in the software industry without precedents. New Machine Learning powered predictive applications are performing jobs that were previously considered exclusive to highly skilled humans.
We are already witnessing a new wave of innovation that is changing the face of all sectors of the economy. To address this market need, BigML in collaboration with Nyenrode Business University in The Netherlands are bringing the first Dutch edition of their Summer School in Machine Learning to The Netherlands.
The goal of this Machine Learning School is to introduce basic as well as more advanced Machine Learning concepts and techniques that will help you boost your productivity significantly.
Where and when?
This four-day event will provide a jam-packed curriculum for different professional profiles.
Attendees can expect a two-day crash course, a C-level course and an opportunity to learn from the masters. The Summer School is aimed at business leaders and senior executives in all industries, experienced business analysts, data scientists, and Machine Learning practitioners, as well as students.
You can sign up for the full package, or purchase seperate tickets.
All lectures will take place at Nyenrode Business Universiteit from 8:30 AM to 5:00 PM CEST on 8 July, 9 and 10 July and 11 July 2019.
For more about the lecturers and to view the full programme, visit the BigML website. We are proud to be a partner!Get your tickets!
Meet the Chairman
Jan W. Veldsink is a master in the art of AI at Nyenrode, Rabobank, and Grio.
He has more than 25 years of experience in digital technology. Veldsink has a passion for technology and people, and his areas of expertise include AI, intelligent systems, robotics, cyber security, and organisational and group dynamics.
“Artificial intelligence, learning algorithms and big data are transforming every aspect of our society and business. In the coming years, these advanced systems will become more user-friendly without losing their complexity.”
Read our interview with Veldsink here.