MACHINE LEARNING
Stampa
Anno immatricolazione
2019/2020
Anno offerta
2019/2020
Normativa
DM270
SSD
ING-INF/05 (SISTEMI DI ELABORAZIONE DELLE INFORMAZIONI)
Dipartimento
DIPARTIMENTO DI INGEGNERIA INDUSTRIALE E DELL'INFORMAZIONE
Corso di studio
COMPUTER ENGINEERING
Curriculum
Data Science
Anno di corso
Periodo didattico
Secondo Semestre (02/03/2020 - 12/06/2020)
Crediti
6
Ore
59 ore di attività frontale
Lingua insegnamento
English
Tipo esame
ORALE
Docente
CUSANO CLAUDIO (titolare) - 6 CFU
Prerequisiti
Students are expected to have a basic knowledge of linear algebra, vector calculus, probability and statistics. They are also expected to be able to design and write simple computer programs.
Obiettivi formativi
At the end of the course students will be able to understand and discuss the principles of machine learning. They will be able to analyze a problem, and to design and implement a solution. They will be familiar with the most important techniques in the field and will be able to use them to build machine learning systems by using the Python programming language.
Programma e contenuti
After a general introduction to machine learning, the first lectures will focus on the main techniques used to tackle the problem of classification by supervised learning. More in detail the following topics will be presented:
- logistic regression;
- generalization and regularization;
- the perceptron algorithm;
- linear and non-linear Support Vector Machines;
- multi-class models;
- cross validation and model selection;
- generative models and naive Bayes.
Artificial neural networks will be the main topic of the second part of the course. The lectures will cover:
- the biological inspiration;
- feed forward networks;
- the backpropagation algorithm;
- introduction to deep learning;
- convolutional neural networks;
- recurrent networks.
The last part of the course will present some application domains in which machine learning models are widely used:
- document classification;
- image recognition;
- computational photography.
Metodi didattici
About two thirds of the course will be given as lectures in which machine learning principles and techniques will be illustrated, even through the presentation of case studies. One third of the course will take place in a laboratory, in which the students will learn to solve machine learning problems by using the Python programming language.
Testi di riferimento
The course is based on a set of notes that are supplemented by a selection of articles.
Modalità verifica apprendimento
The exam consists of an interview in which the student will discuss the topics of the course. During the interview the students will be asked to present their implementation of a solution for a specific machine learning problem.
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