About this course: The Large Hadron Collider (LHC) is the largest data generation machine for the time being. It doesn’t produce the big data, the data is gigantic. Just one of the four experiments generates thousands gigabytes per second. The intensity of data flow is only going to be increased over the time. So the data processing techniques have to be quite sophisticated and unique. In this course we’ll introduce students into the main concepts of the Physics behind those data flow so the main puzzles of the Universe Physicists are seeking answers for will be much more transparent. Of course we will scrutinize the major stages of the data processing pipelines, and focus on the role of the Machine Learning techniques for such tasks as track pattern recognition, particle identification, online real-time processing (triggers) and search for very rare decays. The assignments of this course will give you opportunity to apply your skills in the search for the New Physics using advanced data analysis techniques. Upon the completion of the course you will understand both the principles of the Experimental Physics and Machine Learning much better.
Who is this class for: This course is recommended to those who has been introduced to the Machine Learning methods and skills, and who is interested in applying those to practical scientific research challenges. The field of High Energy Physics is just one fascinating example of modern scientific fields that can be greatly advanced by Machine Learning tools. No special Physics background is required to complete the assignments.
Taught by: Andrei Ustyuzhanin, Head of Laboratory for Methods of Big Data AnalysisHSE Faculty of Computer Science
Taught by: Mikhail Hushchyn, Researcher at Laboratory for Methods of Big Data AnalysisHSE Faculty of Computer Science