Introduction of BDT and Neural Networks for Classification in High Energy Physics - Lecture 2

Explore advanced classification techniques in High Energy Physics (HEP) through this comprehensive lecture on Boosted Decision Trees (BDT) and Neural Networks (NN). Delve into the second part of a series presented by Elham E Khoda and Aishik Ghosh at the International Centre for Theoretical Sciences. Gain valuable insights into the application of machine learning methods for analyzing complex HEP data. Learn how these powerful tools can be utilized to improve particle identification, event classification, and signal-background discrimination in particle physics experiments. Discover the potential of BDTs and NNs to enhance the sensitivity of searches for new physics phenomena and improve measurements of Standard Model processes. Suitable for graduate students, postdoctoral researchers, and professionals in particle physics looking to expand their knowledge of cutting-edge data analysis techniques in HEP.