The module presents the methodological aspects, technologies and systems for designing predictive systems of Artificial Intelligence through machine learning and deep neural networks. The emphasis is placed on the analysis of application problems using examples and case studies, with practical exercises.
Prerequisites: Python & Data Mining & Machine Learning
The course introduces the student to the main concepts of statistical analysis, the methods used and the software implementations to carry out a quantitative and rigorous study of a dataset. After introducing the basic tools of descriptive statistics, the course focuses on probabilistic statistics and its use for data modelling, estimation methods through an inferential approach and statistical hypothesis testing.
The course will deal with time series and spatio-temporal data, in particular mobility. We will illustrate the fundamental characteristics of these two data classes as well as the most common pre-processing and analysis methods. Finally, each lesson will provide examples of use and exercises carried out in Python with the appropriate libraries.
Prerequisites: Data Mining & Machine Learning, Python
Over the past decade, there has been a growing public fascination with the complex “connectedness” of modern society. This connectedness is found in many contexts: in the rapid growth of the Internet and the Web, in the ease with which global communication now takes place, and in the ability of news and information as well as epidemics and financial crises to spread around the world with surprising speed and intensity.
The module presents the methodological aspects, technologies and systems for designing predictive systems of Artificial Intelligence through machine learning and deep neural networks. The emphasis is placed on the analysis of application problems using examples and case studies, with practical exercises.
Prerequisites: Python & Data Mining & Machine Learning
The formidable advances in computing power, data acquisition, data storage and connectivity have created unprecedented amounts of data. Data mining, i.e., the science of extracting knowledge from these masses of data, has therefore been affirmed as an interdisciplinary branch of computer science. Data mining techniques have been applied to many industrial, scientific, and social problems, and are believed to have an ever deeper impact on society.
This module presents artificial intelligence techniques aimed at defining analytics on text and data from the Web. The course is organized around three main strands: i) text analytics, where text mining methods applied to texts and social media are studied; ii) sorting techniques through the application of "learning to rank" techniques which have the purpose of estimating the relevance of objects with respect to user requirements, iii) web mining techniques aimed at exploiting user usage data to improve quality of services.
Il corso presenta le principali tecninche di analisi dei dati sel web.Usando i query log di un motore di ricerca come un caso di studio, gli studenti sono guidati nello sviluppo di un insieme di metodologie per l'analisi dei dati con lo scopo di creare la base di conoscenza utile a costruire un sistema di raccomandazione. Inoltre il corso discute come la stessa informazione possa essere ottimizzata per il ranking nei servizi web.
Il modulo si propone di fornire un’introduzione ai concetti di base del data mining e del processo di estrazione della conoscenza, con approfondimenti sui modelli analitici e gli algoritmi più diffusi per il clustering, la classificazione e la scoperta di patterns, anche in riferimento alle nuove sorgenti di Big Data.
Questo modulo introduce le principali tecniche per l'analisi e il mining delle opinioni degli utenti generate principalmente nel web.
Il corso si focalizza principalmente su metodi di text mining applicati al testo generato nei social media e su tecniche di web mining. Usando dei query log di un motore di ricerca reale come caso di studio, gli studenti saranno guidati nello sviluppo di un insieme di metodologie per l'analisi di dati che ha lo scopo di creare la base di conoscenza necessaria a costruire un sistema di raccomandazione.
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