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.
Rezensionen / Stimmen
'An authoritative treatment of modern machine learning, covering a broad range of topics, for readers who want to use and understand machine learning.' Bernhard Schoelkopf, Max Planck Institute for Intelligent Systems 'This book provides the perfect introduction to modern machine learning, with an ideal balance between mathematical depth and breadth. Its outstanding clarity and many illustrations make it a perfect tool for self-learning or as a textbook for an introductory machine learning class.' Francis Bach, Inria - Ecole Normale Superieure 'Lucid and engaging, this book is a brilliant companion to anyone with a numerate background who wants to know what really goes on under the hood in supervised learning. The core theory and rich illustrative examples enable practitioners navigate the jungle of modern machine learning.' Carl Edward Rasmussen, University of Cambridge 'This book provides an excellent introduction to machine learning for engineers and scientists. It covers the main techniques in this exciting area ranging from basic approaches, such as linear regression and principal component analysis, to modern deep learning and generative modelling techniques. The authors have managed to find the right balance between academic rigor, intuition and applications. Required reading for any newcomer interested in this field!' Arnaud Doucet, University of Oxford '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 'The applied nature of the text and its wide coverage are perfect as both an introductory book and a handbook for industrial implementation. The applied nature of the text and its wide coverage are perfect as both an introductory book and a handbook for industrial implementation.' Karl Jones, Liverpool John Moores University
Auflage
Sprache
Verlagsort
Zielgruppe
Für höhere Schule und Studium
Für Beruf und Forschung
Illustrationen
Worked examples or Exercises
Maße
Höhe: 260 mm
Breite: 183 mm
Dicke: 23 mm
Gewicht
ISBN-13
978-1-108-84360-7 (9781108843607)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Klassifikation
Andreas Lindholm is a machine learning research engineer at Annotell, Gothenburg, working with data annotation and data quality questions for autonomous driving. He received his MSc degree in 2013 from Linkoeping University (including studies at ETH Zuerich and UC Santa Barbara). He received his PhD degree in 2018 from the Department of Information Technology, Uppsala University. At the time of writing this book he was a postdoctoral researcher at the same department. Throughout his entire academic career he has had a particular interest in teaching applied mathematical subjects. Niklas Wahlstroem is an Assistant Professor at the Division of Systems and Control, Department of Information Technology, Uppsala University. He received his MSc degree in 2010 and his PhD degree in automatic control in 2015, both from Linkoeping University, Sweden. During his studies he held a visiting position at ETH Zuerich and Imperial College, London. Since 2016, he has been working at Uppsala University, first as a postdoctoral researcher and since 2019 in his present position. During his career, he has been involved in several machine learning course development projects. Fredrik Lindsten is Associate Professor at the Division of Statistics and Machine Learning, Department of Computer and Information Science, Linkoeping University. He received his PhD in Automatic Control in 2013 from Linkoeping University. In 2014-2015 he was a Postdoctoral Research Associate at the University of Cambridge. He has held visiting positions at the Statistical Artificial Intelligence Lab, UC Berkeley, and the Department of Statistics, University of Oxford. He has received the Ingvar Carlsson Award by the Swedish Foundation for Strategic Research, and the Benzelius Award by the Royal Society of Sciences in Uppsala. Thomas B. Schoen is Beijer Professor of Artificial Intelligence, Department of Information Technology, Uppsala University. He has held visiting positions with the University of Cambridge, the University of Newcastle and Universidad Tecnica Federico Santa Maria in Valparaiso. In 2018, he was elected to The Royal Swedish Academy of Engineering Sciences and The Royal Society of Sciences at Uppsala. He has been awarded the Tage Erlander prize for natural sciences and technology and the Arnberg prize by the Royal Swedish Academy of Sciences. He received the best teacher award at the Institute of Technology, Linkoeping University in 2009. He is a fellow of the ELLIS society.
Autor*in
Uppsala Universitet, Sweden
Linkoepings Universitet, Sweden
Uppsala Universitet, Sweden
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.
