eScience Center holds inspiring workshop on Machine Learning for Research
23 Jan 2020 - 3 min
The Netherlands eScience Center held a workshop on Machine Learning for Research
Advancing research through machine learning: an applied coding workshop
From 20 -24 January, the Netherlands eScience Center held a workshop on Machine Learning for Research at its offices at Amsterdam Science Park.
During the workshop, which took place in a collaborative workspace, six teams from different disciplines and research institutions spent a week of hands-on work with machine learning experts from the eScience Center. Each team came equipped with their own data and went on to an intensive one-week collaboration with machine-learning experts from the eScience Center and SURF to explore the best machine learning strategy to tackle their research question.
The core focus of the workshop was on writing and developing code to analyze the data and apply suitable machine-learning techniques. This hands-on machine-learning experience was complemented by inspiring talks by the Director of the Netherlands eScience Center, Joris van Eijnatten, Maxwell Cai (SURF) on machine and deep learning, Vincent Warmerdam (GoDataDriven) on artificial stupidity, Jakub Tomczak (VU) on deep generative modeling and Florian Huber (eScience Center) on machine learning in research – dealing with the non-ideal.
“The workshop was a great experience. Together with my team, I got to actually focus on the research for 5 days without any interruption. The experts gave us enough time to work with our own data, providing us with good set of starting models we can refine. The talks were inspiring and informative. The trainers didn’t just explore the possibilities of machine Learning but also discussed its pitfalls.” – Eduard Klapwijk
“The best thing is we get to use our own data and work on our own problem instead of a hypothetical problem. It feels like we are actually getting somewhere instead of leaving the workshop with a very abstract view on machine learning tackling research problems” – Niala den Braber
“I was amazed by how motivated, persistent, and curious all teams were in exploring machine-learning options on all the great datasets they brought. Many participants started from a fairly basic understanding of machine-learning, but I really felt that their good knowledge about research and data allowed them to super quickly get a very good intuition about what machine-learning can and cannot do. I hadn’t expected that we would come that far in only one week.” – Florian Huber