Statistics for Data Analysis with Exercises - Lecture 3

Delve into the third lecture of a comprehensive series on Statistics for Data Analysis, presented by Tommaso Dorigo at the International Centre for Theoretical Sciences. Explore advanced statistical methods and their applications in data analysis through practical exercises. Gain insights into techniques crucial for High Energy Physics research, including classification, identification, and estimation strategies used in LHC experiments. Learn how to apply deep machine learning and artificial intelligence frameworks to analyze large-scale datasets. Enhance your skills in programming languages like Python and C++, and familiarize yourself with data analysis tools such as Madgraph, Pythia, Delphes, and ROOT. Benefit from this lecture as part of a broader program aimed at developing human resources in machine learning for High Energy Physics, suitable for PhD students and postdoctoral researchers in theoretical or experimental particle physics and astro-particle physics.