In June 2025, the 17th Machine Learning and Advanced Statistics Summer School took place at the Universidad Politécnica de Madrid (UPM), bringing together students, researchers, and professionals interested in modern machine learning and statistical methods. The event was organized by the Computational Intelligence Group at the Escuela Técnica Superior de Ingenieros Informáticos (UPM) and coordinated by Pedro Larrañaga and Concha Bielza, both members of ELLIS Unit Madrid.
The summer school offered an intensive program designed to introduce participants to both the theoretical foundations and the practical applications of machine learning and advanced statistical techniques currently used in data analysis and modelling. Over two weeks, participants could choose among twelve courses, each consisting of 15 hours of lectures and hands-on sessions, allowing attendees to explore a variety of topics according to their interests. Courses combined theoretical explanations with practical computer-based exercises, providing participants with computational tools to apply the studied techniques to real-world problems.
The program covered a broad range of topics across machine learning and statistics. During the first week, courses addressed areas such as Bayesian Networks, Time Series Analysis, Supervised Classification, Reinforcement Learning, Deep Learning, and Bayesian Inference. The second week focused on topics including Causality, Clustering, Gaussian Processes and Bayesian Optimization, Explainable Machine Learning, Generative AI, and Feature Subset Selection.
The summer school featured instructors from several international institutions, including Universidad Politécnica de Madrid, Universidad Autónoma de Madrid, Linköping University, University of California Riverside, CEU-San Pablo University, Universidad Carlos III de Madrid, CUNEF, and Universidad Pontificia Comillas. Their combined expertise provided participants with a broad perspective on current developments in machine learning and statistical modelling.
Open to students and professionals from a wide range of disciplines—including computer science, engineering, medicine, economics, and statistics—the summer school aimed to strengthen participants’ technical background in data analysis while making the course material accessible to attendees with diverse academic backgrounds.
Through its combination of theoretical instruction, practical training, and interdisciplinary participation, the 17th edition of the summer school continued its tradition of providing a comprehensive introduction to modern machine learning and advanced statistical methods.
More info: MLAS