Machine Learning
A First Course for Engineers and Scientists

Presents carefully selected supervised and unsupervised learning methods from basic to state-of-the-art,in a coherent statistical framework.

Andreas Lindholm (Author), Niklas Wahlström (Author), Fredrik Lindsten (Author), Thomas B. Schön (Author)

9781108843607, Cambridge University Press

Hardback, published 31 March 2022

350 pages
25.9 x 18.2 x 2 cm, 0.88 kg

'This book strikes a very good balance between accessibility and rigour. It will be a very good companion for the mathematically trained who want to understand the hows and whats of machine learning.' Ole Winther, University of Copenhagen and Technical University of Denmark

This book introduces machine learning for readers with some background in basic linear algebra, statistics, probability, and programming. In a coherent statistical framework it covers a selection of supervised machine learning methods, from the most fundamental (k-NN, decision trees, linear and logistic regression) to more advanced methods (deep neural networks, support vector machines, Gaussian processes, random forests and boosting), plus commonly-used unsupervised methods (generative modeling, k-means, PCA, autoencoders and generative adversarial networks). Careful explanations and pseudo-code are presented for all methods. The authors maintain a focus on the fundamentals by drawing connections between methods and discussing general concepts such as loss functions, maximum likelihood, the bias-variance decomposition, ensemble averaging, kernels and the Bayesian approach along with generally useful tools such as regularization, cross validation, evaluation metrics and optimization methods. The final chapters offer practical advice for solving real-world supervised machine learning problems and on ethical aspects of modern machine learning.

1. Introduction
2. Supervised learning: a first approach
3. Basic parametric models and a statistical perspective on learning
4. Understanding, evaluating and improving the performance
5. Learning parametric models
6. Neural networks and deep learning
7. Ensemble methods: Bagging and boosting
8. Nonlinear input transformations and kernels
9. The Bayesian approach and Gaussian processes
10. Generative models and learning from unlabeled data
11. User aspects of machine learning
12. Ethics in machine learning.

Subject Areas: Signal processing [UYS], Machine learning [UYQM], Mathematical theory of computation [UYA], Mathematical modelling [PBWH], Information theory [GPF]