⏱ Deadline: 10 may
The Large Hadron Collider (LHC) is the largest data generation machine for the time being. 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.
MLHEP 2021 Summer school materials
There are plenty of important challenges in high energy physics that can be solved using Machine Learning methods. These vary from online data filtering and reconstruction to offline data analysis. However solving those challenges requires deep understanding of Machine Learning methods and tools that has been developed so far.
Six main sections structure the materials of the school:
- Introduction into Machine Learning
- Introduction into Deep Learning
- Bayesian Deep Learning
- Generative models
- Optimization methods
- Advanced topics
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