Machine Learning Competition: Tennis Prediction
09:00-16:30 January 27

Full day
Intermediate level


With jobs in Data Science ranked among the most lucrative, the competition is intensifying. This one-day workshop will provide participants with the necessary tools to dominate in Machine Learning challenges. It will be hosted by the founders of L2F-Learn to Forecast, a group of EPFL Mathematicians who came to prominence by winning the New York City Taxi Challenge on Kaggle in 2017 over more than 1200 teams. L2F now has 25 employees and delivers advanced ML solutions to large international corporations and institutions; it recently won the Siemens Competition on IoT and Predictive Maintenance in Berlin.

Each L2F founder will lead a team of participants and compete against the others. The goal will be to predict the outcome of tennis games based on a curated ATP World Tour data set. The most creative feature engineering will make the difference!


Solid understanding of the process required to succeed in competitive data science: data processing, exploratory data analysis, feature creation, modeling, cross-validation. Special emphasis on creative thinking and team collaboration.


  • Basic knowledge of Machine Learning
  • Experience with Python (numpy, pandas, scikit-learn)
  • Personal computer, with Jupyter Notebook installed


Maxime Gabella

Chief Scientist, L2F