Machine learning applications in physics are becoming an important part of modern experimental high energy physics analyses. To that extent, as I start to seriously begin the bulk of my Ph.D. thesis work in the ATLAS collaboration in the VH→bb̅ and hh→(bb̅)(bb̅) analysis working groups I am teaching myself about the fundamentals of machine learning and appropriate machine learning tools. I will document and share how I learn this information here.
To get started, I've begun a listing of resources for machine learning applications in high energy physics (HEPML) to act as a self reference and reading list. Contributions from other physicists and machine learning researchers in HEP are greatly welcome!
This summer I will be attending 3 physics workshops that will either focus on machine learning applications in HEP, or have sessions devoted to machine learning. I'm greatly looking forward to them and this "Summer of machine learning (applications in particle physics)".