Machine learning is programming computers to optimize a performance criterion using example data or past experience. We need learning in cases where we cannot directly write a computer program to solve a given problem, but need example data or experience. Another case is when the problem to be solved changes in time, or depends on the particular environment. In this Chapter we introduce the basic components of machine learning and focus on a few very popular machine learning models: decision trees, random forest (tree models) and support vector machines (geometric and linear models).

Introduction to Machine Learning

Elena Bellodi;Riccardo Zese;Fabrizio Riguzzi;Evelina Lamma
2022

Abstract

Machine learning is programming computers to optimize a performance criterion using example data or past experience. We need learning in cases where we cannot directly write a computer program to solve a given problem, but need example data or experience. Another case is when the problem to be solved changes in time, or depends on the particular environment. In this Chapter we introduce the basic components of machine learning and focus on a few very popular machine learning models: decision trees, random forest (tree models) and support vector machines (geometric and linear models).
2022
9783031038402
supervised learning, decision trees, random forest, SVM
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11392/2501379
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