Machine learning (ML) has become a commonplace element in our everyday lives and a standard tool for many fields of science and engineering. To make optimal use of ML, it is essential to understand its underlying principles.
This book approaches ML as the computational implementation of the scientific principle. This principle consists of continuously adapting a model of a given data-generating phenomenon by minimizing some form of loss incurred by its predictions.
The book trains readers to break down various ML applications and methods in terms of data, model, and loss, thus helping them to choose from the vast range of ready-made ML methods.
The book's three-component approach to ML provides uniform coverage of a wide range of concepts and techniques. As a case in point, techniques for regularization, privacy-preservation as well as explainability amount tospecific design choices for the model, data, and loss of a ML method.

Proposes a simple three-component approach to formalizing machine learning problems and methods Interprets typical machine learning methods using the unified scientific cycle model: forming hypothesis Covers hot topics such as explainable and privacy-preserving machine learning

Autorentext

Alexander Jung ist Professor für maschinelles Lernen am Institut für Computer Science der Aalto-Universität, wo er die Forschungsgruppe "Maschinelles Lernen für Big Data" leitet. Seine Kurse über maschinelles Lernen, künstliche Intelligenz und konvexe Optimierung gehören zu den beliebtesten Kursen an der Aalto-Universität. Er erhielt 2011 einen Best Student Paper Award auf der Premium-Signalverarbeitungskonferenz IEEE ICASSP, 2018 einen Amazon Web Services Machine Learning Award und wurde 2018 zum "Computer Science - Teacher of the Year" gewählt. Er ist Associate Editor für die IEEE Signal Processing Letters und Editorial Board Member des Springer Journals "Machine Learning".

Inhalt
Introduction.- Components of ML.- The Landscape of ML.- Empirical Risk Minimization.- Gradient-Based Learning.- Model Validation and Selection.- Regularization.- Clustering.- Feature Learning.- Transparant and Explainable ML.