perClass Machine Learning Course
Hands-on real-world training

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Eighteen editions of the course (since 2012) were attended by participants from organizations in 16 countries.


Summary of internal Philips evaluation for July 2016 course (scale 1 to 5, 5=excellent)

"Our team on Vision & Robotics choose to use perClass in our machine vision solutions on a structural base. Therefore we followed the in-house training at Wageningen UR with the whole team of (senior) software researchers. The in-house training was from a very high level, perfectly fitting the market demands. Now we should be able to develop faster with a better score  on classification issues in agriculture and food applications.
-- Erik Pekkeriet, Senior Project manager at Wageningen UR, The Netherlands

"Thanks again for the excellent course. I gained the right knowledge to delve further into our machine learning problems."
-- Dr Stefan Gachter, Leica, Switzerland

"I found the course very informative. I think you have done an excellent job in developing a logical framework, with powerful tools,
for tackling pattern recognition problems. The use of examples from real problems is extremely useful in understanding the strengths and limitations of a range of concepts and approaches. Thanks again for the great course!"

-- Dr Garry Morrison, DeBeers, South Africa

"A comprehensive course in applying machine learning for classification tasks, providing the relationship between different methods, pointers into when to apply what, and best practices in gradually moving from the simplest applicable approach to more complex
methods. The major contribution of this course is the exchange of knowledge gained from years of experience into identifying common pitfalls and the specific ways of fixing them. The perClass tool is used within MATLAB as an interactive configuration editor, after which one applies the exported configuration file directly in the standalone C library."

-- Dr.Edgar Reehuis, Incatec, The Netherlands