CODICE CORSO: LINGUA:

An Introduction to Machine Learning in Stata

Over the last few years we have experienced an unprecedented explosion in the availability of data relating to social, economic, financial and health-related phenomena. Today researchers, professionals and policy makers have therefore access to enormous datasets (so-called Big Data) containing an abundance of information regarding individuals, companies and institutions.

 

Machine learning has evolved in response to both the need to analyse extremely large databases and the availability of both sophisticated software and extremely powerful computer capacity. Machine learning, an application of artificial intelligence, offers a relatively new approach to data analysis, which trains systems to automatically learn and improve from experience without being explicitly programmed, relying instead on patterns and inference in the data.

 

This workshop offers participants an introduction to both machine learning techniques and the commands for Machine Learning recently introduced in Stata 16.

Il workshop è di particolare interesse per ricercatori e professionisti in biostatistica, economia, marketing, scienze sociali e sanità pubblica che desiderano acquisire gli strumenti fondamentali per implementare l’approccio di machine learning sui così detti Big Data.

Conoscenza di base di econometria/statistica e del software Stata.

SESSION 1: EXAMPLES OF MACHINE LEARNING METHODOLOGIES

This opening session focuses on the more popular supervised and unsupervised Machine Learning (ML) techniques, and their implementation in Stata. This session focuses on regression trees, random forests and cluster analysis.

 

SESSION 2: REGULARIZED REGRESSION

Regularized regression and the Lasso approach play a central role in Machine Learning. This session is devoted therefore to Lasso, Elastic Net and related methodologies. We will demonstrate their application in Stata using both the user written Lassopack commands and Stata 16’s new Machine Learning routines.

 

SESSION 3: CAUSAL INFERENCE WITH MACHINE LEARNING

The primary strength of Machine Learning is prediction. In this session, we illustrate how Lasso and other Machine Learning methodologies can also be used to facilitate causal inference. The workshop concludes by looking at the latest developments in the evolving literature on Machine Learning and causal inference.

 

Il Corso si terrà a Firenze il 27 Settembre 2019 presso l’Hotel Brunelleschi, Piazza S. Elisabetta 3.

Il materiale didattico distribuito include le dispense con la parte teorica, i do file e le banche dati per l’implementazione di tutte le applicazioni empiriche e una licenza temporanea del Software Stata 16 valida per 30 giorni dall’inizio del corso. Data la natura applicata del corso si consiglia l’utilizzo del proprio personal computer per seguire autonomamente le sessioni applicate.

 

Il numero massimo di iscritti ammessi al Corso di Formazione è 15, ed il termine per presentare la propria richiesta di ammissione è il 15 Settembre 2019.

 

Per ulteriori informazioni consultare la pagina del convegno https://www.tstat.it/utenti/XVI-convegno-italiano-degli-utentidi-stata/ oppure contattare la segreteria organizzativa a formazione@tstat.it.


L’iscrizione al corso dovrà avvenire tramite lo specifico modulo di registrazione e pervenire a TStat S.r.l. almeno 15 giorni prima dell’inizio del corso stesso. E’ possibile richiedere il modulo di registrazione compilando il seguente form oppure inviando una mail a formazione@tstat.it


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Over the last few years we have experienced an unprecedented explosion in the availability of data relating to social, economic, financial and health-related phenomena. Today researchers, professionals and policy makers have therefore access to enormous datasets (so-called Big Data) containing an abundance of information regarding individuals, companies and institutions.

 

Machine learning has evolved in response to both the need to analyse extremely large databases and the availability of both sophisticated software and extremely powerful computer capacity. Machine learning, an application of artificial intelligence, offers a relatively new approach to data analysis, which trains systems to automatically learn and improve from experience without being explicitly programmed, relying instead on patterns and inference in the data.