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Overview: This book offers a leisurely introduction to the concepts and methods of Machine Learning. Readers will learn about classification trees, Bayesian learning, neural networks and Deep Learning, the design of experiments, and related methods. For ease of reading, technical details are avoided as far as possible, and there is a particular emphasis on applicability, interpretation, reliability and limitations of the data-analytic methods in practice. To cover the common availability and types of data in engineering, training sets consisting of independent as well as time series data are considered. To cope with the scarceness of data in industrial problems, augmentation of training sets by additional artificial data, generated from physical models, as well as the combination of Machine Learning and expert knowledge of engineers are discussed. This book is dedicated to statistical learning in engineering applications. Its main purpose is to introduce engineers to this exciting new area without requiring more than the most basic training in mathematics and statistics, which usually is part of the engineering education. On this level, it is also of interest for students of Data Science or related disciplines as a first primer on statistical learning with a focus on ideas and not on technical details. In the first part, a short leisurely introduction to statistical learning as a part of Machine Learning, particularly important for industrial users, is given. The second part of the book presents case studies in applications of statistical learning from projects with industry from the Fraunhofer Institute of Industrial Mathematics (ITWM). As a software, we use basic MATLAB and its toolbox Statistics and Machine Learning. For readers who, for the first time, are interested in applying statistical learning procedures themselves, we would recommend as a starting point R or Python due to the large variety of ready-made routines instead. The focus is on fundamental ideas, applicability and the pitfalls of Machine Learning in industry and science, where data are often scarce. Requiring only very basic background in statistics, the book is ideal for self-study or short courses for engineering and science students.
Genre: Non-Fiction > Tech & Devices
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