McClarren R.G. Machine Learning for Engineers: Using Data to Solve Problems for Physical Systems

Springer, 2021. — 252 p. — ISBN: 978-3-030-70387-5.All engineers and applied scientists will need to harness the power of machine learning to solve the highly complex and data-intensive problems now emerging. This text teaches state-of-the-art machine learning technologies to students and practicing engineers from the traditionally “analog” disciplines — mechanical, aerospace, chemical, nuclear, and civil. Dr. McClarren examines these technologies from an engineering perspective and illustrates their specific value to engineers by presenting concrete examples based on physical systems. The book proceeds from basic learning models to deep neural networks, gradually increasing readers’ ability to apply modern machine learning techniques to their current work and to prepare them for future, as yet unknown, problems. Rather than taking a black-box approach, the author teaches a broad range of techniques while conveying the kinds of problems best addressed by each. Examples and case studies in controls, dynamics, heat transfer, and other engineering applications are implemented in Python and the libraries sci-kit-learn and TensorFlow, demonstrating how readers can apply the most up-to-date methods to their own problems. The book equally benefits undergraduate engineering students who wish to acquire the skills required by future employers, and practicing engineers who wish to expand and update their problem-solving toolkit.Fundamentals
The Landscape of Machine Learning
Linear Models for Regression and Classification
Decision Trees and Random Forests for Regression and Classification
Finding Structure Within a Data Set: Data Reduction and Clustering
Neural Networks
Feed-Forward Neural Networks
Convolutional Neural Networks for Scientific Images and Other Large Data Sets
Advanced Topics
Recurrent Neural Networks for Time Series Data
Unsupervised Learning with Neural Networks: Autoencoders
Reinforcement Learning with Policy GradientsData and Implementation of the Examples and Case Studies