The program of the summer school is divided in two tracks: “Introductory” and “Advanced”. The Introductory track is intended to cover some basic aspects and allows the participant to get familiar with the techniques and tools available in:
- Classification / Regression models
- Algorithm composition, boosting
- Model evaluation, significance estimation
- Neural Networks introduction
- Density estimation algorithms
- Model overfitting detection and mitigation
The Advanced track covers some more detailed and more sophisticated Machine Learning algorithms including:
- Semi-supervised / unsupervised approaches
- Deep learning approach
- Kernel tricks for density estimations, Support Vector Machines
- Decorrelation of variables and predictions
- Feature selection / Dimensionality reduction
Each lecture will be accompanied by a seminar, which will allow for students to gain practical experience and participate in group discussions. Students will have to bring their own notebooks (laptops) to participate in the seminars.
In addition to the Machine Learning lectures and seminars, a variety of talks will be provided by scientists who apply Machine Learning methods to solve particular problems in High Energy Physics.
During MLHEP school our participants will have opportunity to join and compete in data challenge organized for this event.