Fundamentals of Supervised Machine Learning

This book presents the fundamental theoretical notions of supervised machine learning along with a wide range of applications using Python, R, and Stata. It provides a balance between theory and applications and fosters an understanding and awareness of the availability of machine learning methods over different software platforms.

After introducing the machine learning basics, the focus turns to a broad spectrum of topics: model selection and regularization, discriminant analysis, nearest neighbors, support vector machines, tree modeling, artificial neural networks, deep learning, and sentiment analysis. Each chapter is self-contained and comprises an initial theoretical part, where the basics of the methodologies are explained, followed by an applicative part, where the methods are applied to real-world datasets. Numerous examples are included and, for ease of reproducibility, the Python, R, and Stata codes used in the text, along with the related datasets, are available online.

The intended audience is PhD students, researchers and practitioners from various disciplines, including economics and other social sciences, medicine and epidemiology, who have a good understanding of basic statistics and a working knowledge of statistical software, and who want to apply machine learning methods in their work.

Presents the fundamental notions of supervised machine learning Provides a balance between the theory and applications of machine learning using Python, R, and Stata Fosters an understanding and awareness of machine learning methods over different software platforms

Auteur

Dr. Giovanni Cerulli is a Senior Researcher at the CNR-IRCrES, Research Institute on Sustainable Economic Growth, National Research Council of Italy in Rome. His research interests are in applied econometrics, with a special focus on causal inference and machine learning. He has developed original causal inference models, such as dose-response and treatment models with social interaction, and has carried out many Stata commands for causal inference and machine learning. He has published articles in several high-quality scientific journals, and a book: Econometric Evaluation of Socio-Economic Programs: Theory and Applications. He is currently the Editor-in-Chief of The International Journal of Computational Economics and Econometrics.

Contenu
Preface.- The Ontology of Machine Learning.- The Statistics of Machine Learning.- Model Selection and Regularization.- Discriminant Analysis, Nearest Neighbor and Support Vector Machines.- Tree Modelling.- Artificial Neural Networks.- Deep Learning.- Sentiment Analysis.- Index.