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The first three chapters provide an accessible introduction to signatures with a view towards applications in machine learning and statistics, suitable for readers at the level of a master student in mathematics. The chapters in the second part assume knowledge on the signature as contained in the first three chapters. They are otherwise self-contained and can be read independently from each other. Only the second half of the book focuses on mathematical finance applications.
The resource is structured as follows:
On PartI: Introduction to Signatures in Machine Learning
Part I provides a general introduction to signatures and their use in machine learning.
Part II presents various applications of signature methods in finance.

We provide an introduction to the signature method, focusing on its theoretical properties and machine learning applications. Our presentation is divided into two parts. In the first part, we present the definition and fundamental properties of the signature of a path. The signature is a sequence of numbers associated with a path that captures many of its important analytic and geometric properties. As a sequence of numbers, the signature serves as a compact description (dimension reduction) of a path. In presenting its theoretical properties, we assume only familiarity with classical real analysis and integration, and supplement theory with straightforward examples. We also mention several advanced topics, including the role of the signature in rough path theory. In the second part, we present practical applications of the signature to the area of machine learning. The signature method is a non-parametric way of transforming data into a set of features that can be used in machine learning tasks. In this method, data are converted into multi-dimensional paths, by means of embedding algorithms, of which the signature is then computed. We describe this pipeline in detail, making a link with the properties of the signature presented in the first part. We furthermore review some of the developments of the signature method in machine learning and, as an illustrative example, present a detailed application of the method to handwritten digit classification.