Prof. Eric Medvet

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: HTML CSS JavaScript WebServer NodeJS Framework JS (angular/vue)

• Slide del docente, disponibili liberamente online (sul sito del docente: in inglese.

Argomenti del programma: Cos'è Java Concetti principali della programmazione orientata agli oggetti Pacchetti e altri concetti Principi di input/output (I/O); flusso del programma; operazioni di base Stile del codice Java Ereditarietà e polimorfismo Passaggio di parametri; visibilità e ciclo di vita Memoria della JVM: stack e heap Stream per I/O; accesso al file system; I/O di bytes e tipi di dato primitivi Principi di gestione delle…

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.