The last few years have seen the deep learning revolution come to HEP and the LHC. New algorithms and new approaches are being proposed to solve a wide array of problems, including jet tagging, event classification, pileup reduction, event generation, calorimeter simulation, and anomaly detection. The goal of this course is to provide an overview of these various deep learning applications at the LHC, with an eye towards current trends in research. Along the way, students will receive a practical introduction to fundamental concepts and methods in machine learning and data science. Hopefully, after taking this semester-long course, students will be able to understand the latest ML4HEP results on the arXiv and possibly even embark on research projects of their own.
ANNOUNCEMENTS
The first meeting of this course will be THURSDAY, JANUARY 19, 2023.
Instructor:
Lectures: MTh 12:10-1:30, NHETC SEMINAR ROOM
Resources:
Prerequisites:
You will be expected to be at least somewhat familiar with python and some of its essential packages (numpy, matplotlib, ...). A
background in collider physics would also be helpful, although I will attempt to review the basics in the first few lectures.
You will NOT be required to know ROOT or any physics simulation tools (such as Madgraph, Pythia and Delphes). You will also NOT be required to know in advance any of the deep learning software frameworks such as Keras, Tensorflow and pytorch.
Homeworks: TBD
Exams: TBD
Students with disabilities: Please read here.
LIST OF TOPICS
Here is a list of topics I will aim to cover in the course.
This list is incomplete and approximate.
Note it is not a syllabus! In particular the order of topics might be different in the course.
LECTURE NOTES
Here is a link to the webpage of the version of the course I offered in 2021. It contains detailed lecture notes and other information.