Explore the application of Boosted Decision Trees (BDT) and Neural Networks (NN) for classification in High Energy Physics (HEP) through this comprehensive lecture. Delve into advanced statistical methods and machine learning techniques crucial for analyzing large datasets in particle physics experiments. Learn how these powerful tools are employed to identify and classify particles, events, and potential signals of new physics. Gain insights from experts Elham E Khoda and Aishik Ghosh as they discuss the implementation and advantages of BDTs and NNs in HEP research. Understand the importance of these techniques in processing and interpreting the vast amounts of data generated by experiments like the Large Hadron Collider. Suitable for graduate students, postdoctoral researchers, and professionals in theoretical or experimental particle physics and astro-particle physics, this lecture is part of a broader program aimed at developing human resources in deep machine learning and artificial intelligence for HEP.
Introduction of BDT and NN for Classification in HEP
International Centre for Theoretical Sciences via YouTube
Overview
Syllabus
Introduction of BDT and NN for Classification in HEP by Elham E Khoda & Aishik Ghosh
Taught by
International Centre for Theoretical Sciences
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