The book (whose abridged handbook version is freely available here) is dedicated to all researchers interested in machine learning who are not content with only running lines of (deep learning) code but who are eager to learn about this discipline’s assumptions, limitations, and perspectives.

The book aims to introduce students (at Master or PhD level) with the most important theoretical and applied notions to understand how, when and why machine learning algorithms work. The book contains 16 chapters, several appendices and is accompanied by a GitHub R package.

After an introductory chapter, Chapter 2 introduces the problem of extracting information from observations from an epistemological perspective. Chapter 3 summarises the relevant background material in probability. Chapter 4 introduces graphical modelling, a flexible and interpretable way of representing large variate problems in probabilistic terms. Chapter 5 introduces the classical parametric approach to parametric estimation and hypothesis testing. Chapter 6 introduces the Bayesian parametric approach to parametric estimation, hypothesis testing and decision theory. Chapter 7 presents some nonparametric alternatives to the parametric techniques discussed in Chapter 5 and 6. Chapter 8 introduces supervised learning as the statistical problem of assessing and selecting a hypothesis function on the basis of input/output observations. Chapter 9 reviews the steps which lead from raw observations to a final model. This is a methodological chapter that introduces some algorithmic procedures underlying most of the machine learning techniques. Chapter 10 presents conventional linear approaches to regression and classification. Chapter 11 introduces the most common machine learning techniques which deal with nonlinear regression and classification tasks. Chapter 12 presents the model averaging approach, a recent and powerful way for obtaining improved generalisation accuracy by combining several learning machines. Chapter 13 deals with the problem of dimensionality reduction and in particular with feature selection strategies. Chapter 14 introduces stochastic processes and deals with the use of machine learning techniques for time series forecasting. Chapter 15 presents the problem of estimation in dynamic

settings (notably Kalman filter and HMM). Chapter 16 presents the relation between machine learning and causal inference by highlighting the risks of interpreting observational models in a causal manner. Although the book focuses on supervised learning, some related notions of unsupervised learning and density estimation are presented in Appendix A.

Several scripts and dashboards are used in the main text to illustrate statistical and machine learning notions. All the scripts have been implemented in R, Shiny and are available in a companion R GitHub package.