Machine learning as an extra gear for science

Philip Hans Franses

Data is booming, almost all sectors seem to be focusing more and more on data collection. The financial sector is of course fully involved in this. In the VBA Journaal, Philip Hans Franses, Professor of Applied Econometrics and Professor of Marketing Research at Erasmus School of Economics, gives an insight about the most important developments in the field of data analysis. He talks about the transformation from searching in science for 1 exact solution to searching through a huge spectrum of data for multiple solutions.

"An algorithm is nothing more than some kind of decision rule. For example: when it rains, I take an umbrella with me. In a certain way this is also an algorithm," says Franses. "Data analyses, choices in data and managing data is something we have been doing for decades". According to him we can see machine learning as an extra boost for all sciences imaginable. Machine learning should not be seen as a replacement, but as a possibility to extend existing sciences.

As a result of the introduction of machine learning academic education has changed. Instead of mapping a model with a handful of variables, a model can contain up to 3000 variables. A student is asked to find the best predictive variable out of all these variables. Franses believes this brings matter closer to practice. To be up to date with the latest academic techniques, Franses sees a need to add machine learning to the curriculum.

Every day, new data techniques are added and used which can be overwhelming. It is important for the scientific world to map these new developments. For the upcoming years this will certainly be one of the most important tasks, so concludes Franses.

About the VBA Journaal:
The VBA Journaal is contains articles about relevant developments in the investment industry, practice-oriented articles, research and opinion.

Professor
Philip Hans Franses
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The entire article can be downloaded above, autumn 2019 (in Dutch).

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