Machine Learning for Economics, Finance and Management workshop (In-Person) with Professor Sourafel Girma (Registration Closed)

Date(s) - 23/05/2024 - 24/05/2024 All Day

Leeds University Business School


The emergence of big data is posing exciting challenges and opportunities to businesses and researchers alike. Machine learning tools are at the heart of big data analysis and are becoming indispensable for a more nuance understanding of the working of the economy and informed decision making. 

Machine learning algorithms can deal with Big Data that are characterised by large volume, high dimension, and complex structures such as unstructured   data (texts, web-search queries and pictures that measure some sort of economic activity). This requires the acquisition of advanced programming and computational skills. 

 In contrast to traditional econometric techniques, machine learning algorithms make systematic model comparisons and attempt at learning from different models at the same time, leading to concepts such as model averaging and ensemble method. This is not a trivial problem and needs understanding advanced statistical concepts such as parameter tuning, K-fold cross validation and bootstrapping. 

 Without sacrificing rigour, the workshop seeks to be as inclusive as possible and provides a solid introduction to supervised machine learning for financial, economic, and management data analysis using R. By the end of the workshop participants should be able to develop supervised machine learning models for a variety of prediction and classification problems.  

Professor Sourafel Girma is Professor of Industrial Economics at the University of Nottingham and Fellow of the Kiel Centre for Globalization. Trained as an econometrician, Professor Girma’s research interests and expertise lie in the area of applied micro-econometrics with special focus on firm level adjustment to the process of globalisation and international industrial organisationHe is a world known expert in policy evaluation methods for observational studies and other advanced micro-econometrics techniques for longitudinal dataHis work has been extensively published in top international journals including Journal of Econometrics, Journal of International Economics, Journal of International Business Studies, Journal of Industrial Economics, World Bank Economic Reviewand European Economic Review, amongst others.