Lecture notes on Machine Learning applications for global fits
This paper presents a comprehensive framework for accelerating high-energy physics global fits by employing Machine Learning surrogates, specifically Boosted Decision Trees trained via active learning, to approximate log-likelihood functions and efficiently explore parameter spaces, as demonstrated by an application to the anomaly and Axion-Like Particles at Belle II.