Minicourse 20  Isabel Serra 

Learning statistics for AI/ML

Statistics, as a field, serves as the backbone of modern data analysis, providing the means to distill complex information into meaningful insights and predictions. Throughout its evolutionary journey, statistics has continually adapted and refined its methodologies to meet the ever-growing demands of an increasingly data-driven world.

From its humble origins as a tool for summarizing data sets, statistics has evolved into a powerful framework for inference. Its role in quantifying uncertainty has been fundamental in shaping our understanding of the world around us. Whether in the realm of science, economics, sociology, or beyond, statistics has proven indispensable in extracting knowledge.

The advent of the digital age has heralded a new era in statistical analysis, with the proliferation of computing technology enabling the handling of massive datasets with unprecedented ease. This has paved the way for the rise of artificial intelligence (AI) and machine learning (ML) algorithms, which leverage statistical principles to extract meaningful insights from complex data structures. The synergy between statistics and AI/ML has revolutionized fields as diverse as healthcare, finance, and marketing, driving innovation and transformation at an unprecedented pace. Concretaly, while traditional statistics has indeed largely centered its developments around characterizing uncertainty through patterns or mean values, other dimensions within this discipline, notably the study of extreme values, are now offering profound insights.

Prof. Isabel Serra's short biography

Isabel Serra has a Ph.D. in mathematics. She is a professor at the Autonomous University of Barcelona and researches in the Statistical Modeling of Extreme Events and Health Risks group founded by the Spanish government. She is a collaborating researcher in the Complex Systems Group of the Center for Mathematical Research (CRM) since 2014 and in the CAOS group of the BSC since 2018. She was dedicated to the transfer of mathematical knowledge and therefore worked as Head of the Knowledge and Technology Transfer Unit in CRM until 2022. Her main line of research within mathematics is the Theory of Extreme Values and her scientific interest focuses on the study of Complex Systems together with physical researchers and Critical Systems together with engineering researchers. She has worked on several interdisciplinary projects and has published in journals in different fields.

Video of Lecture 1, 2 of May 2024
Video of Lecture 2, 7 of May 2024
Video of Lecture 3  9 of May 2024
Video of Lecture 4, 14 of May 2024