Metodi statistici per l'apprendimento

A.A. 2018/2019
Insegnamento per
6
Crediti massimi
48
Ore totali
SSD
INF/01
Lingua
Inglese
Obiettivi formativi
Machine learning is concerned with the design of algorithms that can predict the evolution of a phenomenon based of a set of observations. Machine learning is a standard tool in the development of intelligent systems. It has been successfully applied to a wide range of domains, including vision, human-computer interaction, product recommendation, health, autonomous navigation, and many more. The course will describe and analyze, in a rigorous statistical framework, the most important machine learning techniques. This will provide the student with a rich set of methodological tools for understanding the general phenomenon of learning in machines.

Struttura insegnamento e programma

Linea Milano
Edizione attiva
INF/01 - INFORMATICA - CFU: 6
Lezioni: 48 ore
Programma
Introduction
Nearest Neighbour
Tree predictors
Cross validation
Statistical risk
Risk analysis for tree predictors
Risk analysis for Nearest Neighbor
Consistency
Compression bounds
Linear classifiers
Online gradient descent
From sequential risk to statistical risk
Kernel functions
Support Vector Machines
Stability bounds and risk in SVM
Boosting
Neural networks and deep learning
Dimensionality reduction
Propedeuticità
Calculus, discrete mathematics, statistics.
Prerequisiti e modalità di esame
The exam is written and oral (both parts are mandatory). The written part involves the preparation of a report describing either a set of theoretical results or the outcome of experiments on real datasets. The oral part includes a detailed discussion of the report and general questions on the course contents.
Metodi didattici
Exam; written and oral.
Attendance: highly recommended.
Teaching modality: traditional.
Materiale didattico e bibliografia
Shai Shalev-Shwartz e Shai Ben-David, Understanding Machine Learning: From Theory to Algorithms, Cambridge University Press, 2014.

Dispense fornite dal docente.
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