Machine learning, statistical learning, deep learning and artificial intelligence

A.A. 2019/2020
Insegnamento per
12
Crediti massimi
80
Ore totali
SSD
INF/01 SECS-S/01
Lingua
Inglese
Obiettivi formativi
The aim of the course is to introduce students to algorithms and statistical tools which are frequently applied in machine learning context.

During the course some lectures will be devoted to explain the different techniques from a theoretical point of view, to enforce the comprehension of the subject. In addition, some laboratories will be held to develop the skills for using a software and for providing statistical interpretations of the outputs.

At the end of the course a student will be able to solve a practical problem by applying algorithms and analyzing data sets.

Struttura insegnamento e programma

Edizione attiva
Moduli o unità didattiche
Module Machine Learning
INF/01 - INFORMATICA - CFU: 6
Lezioni: 40 ore

Module Statistical Learning, Deep Learning and Artificial Intellingence
SECS-S/01 - STATISTICA - CFU: 6
Lezioni: 40 ore
Docente: Salini Silvia

STUDENTI FREQUENTANTI
Prerequisiti e modalità di esame
The exam consists in analyzing a data set applying the techniques learnt during the course. A report concerning the analyzed problem and the applied methods must be delivered to professor and then an oral presentation is required.

Basic statistical knowledge and programming are required to understand the course.
Metodi didattici
Classroom and laboratories
Module Statistical Learning, Deep Learning and Artificial Intellingence
Programma
Regression analysis
Regularization methods: Lasso, Lars, Elastic Net
Classification methods
Tree-based methods (cart, random forests, etc)
Dimensionality reduction methods
Neural-Networks
Deep-Learning
Bayesian learning and principles of Artifical Intelligence.
Metodi didattici
Classroom and laboratories
Materiale didattico e bibliografia
Suggested books (online versions are available):
1) An introduction to statistical Learning, James, Witten, Hastie and Tibshirani, Springer
2) Pattern recognition and machine Learning, Bishop, Springer
Handouts and online resources
STUDENTI NON FREQUENTANTI
Prerequisiti e modalità di esame
The exam consists in analyzing a data set applying the techniques learnt during the course. A report concerning the analyzed problem and the applied methods must be delivered to professor and then an oral presentation is required.

Basic statistical knowledge and programming are required to understand the course.
Module Statistical Learning, Deep Learning and Artificial Intellingence
Programma
Regression analysis
Regularization methods: Lasso, Lars, Elastic Net
Classification methods
Tree-based methods (cart, random forests, etc)
Dimensionality reduction methods
Neural-Networks
Deep-Learning
Bayesian learning and principles of Artifical Intelligence.
Materiale didattico e bibliografia
Suggested books (online versions are available):
1) An introduction to statistical Learning, James, Witten, Hastie and Tibshirani, Springer
2) Pattern recognition and machine Learning, Bishop, Springer
Handouts and online resources
Periodo
Secondo semestre
Periodo
Secondo semestre
Modalità di valutazione
Esame
Giudizio di valutazione
voto verbalizzato in trentesimi
Docente/i
Ricevimento:
Mercoledì 9:30-12:30
via Celoria 18, stanza 7007
Ricevimento:
Mercoledì 10.30-12.00 e il venerdì dale 10.30-12.00. Il giorno 30/10 il ricevimento è sospeso per recupero lezione nello stesso orario, il giorno 1/11 è vacanza accademica. Il ricevimento questa settimana si svolgerà il giovedì 31/10 dalle 10.30 alle 12
DEMM, stanza 31, 3° p