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- You will learn how a multivariate analysis algorithm works (see the below introduction) and more specifically how a Machine Learning model must be implemented;
- you will acquire basic knowledge about the *Higgs boson physics* as as described by the Standard Model. During the exercise you will be invited to plot some physical quantities in order to understand what is the underlying Particle Physics problem;
- you will be invited to *change hyperparameters*hyperparameters of the ANN and the RF parameters algorithms to understand better what are the consequences in terms of the models' performances;
- you will understand that the choice of the *input variables* is a the key task of a Machine Learning to the goodness of the algorithm since an optimal choice allows achieving the best possible performances;
- moreover, you will have the possibility of changing the background datasets, the decay channels of the final state particles, and seeing how the ML algorithms' performance changes.
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Multivariate Analysis algorithms receive as input a set of discriminating variables. Each variable alone does not allow to reach an optimal discrimination power between two categories (we will focus on a binary task in this exercisesignal and background). Therefore the algorithms compute an output that combines the input variables.
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