Argomenti del programma: - Definition of Machine Learning; examples of applications of ML; taxonomy of ML problems; phases of design, development, and assessment of a ML system; terminology and mathematical notation for the key concepts. - Supervised learning. - Assessment. - Tree-based methods. - Support Vector Machines (SVM). - Naive-Bayes classification. - The K-nearest neighbors classifier. - Unsupervised learning.
Argomenti del programma: - Definition of Machine Learning; examples of applications of ML; taxonomy of ML problems; phases of design, development, and assessment of a ML system; terminology and mathematical notation for the key concepts. - Supervised learning. - Assessment. - Tree-based methods. - Support Vector Machines (SVM). - Naive-Bayes classification. - The K-nearest neighbors classifier. - Unsupervised learning.
Corso di laurea: Chimica – sede di Trieste, Sede di Trieste · Laurea magistrale (LM-54) · 2º anno · 1º semestre · Schema di piano individuale (curr. Nanomateriali, energia e modelling) · 6 CFU / esame facoltativo · 6 CFU
Argomenti del programma: - Definition of Machine Learning; examples of applications of ML; taxonomy of ML problems; phases of design, development, and assessment of a ML system; terminology and mathematical notation for the key concepts. - Supervised learning. - Assessment. - Tree-based methods. - Support Vector Machines (SVM). - Naive-Bayes classification. - The K-nearest neighbors classifier. - Unsupervised learning.
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