Andreas Lindholm, Niklas Wahlstroem (Sweden Uppsala Universitet), Fredrik Lindsten (Sweden Linkoepings Universitet)

Machine Learning

A First Course for Engineers and Scientists

• Gebundenes Buch

Produktbeschreibung

This coherent introduction to machine learning for readers with a background in basic linear algebra, statistics, probability, and programming is suitable for advanced BSc or MSc courses. It covers theory and practice of basic and advanced methods such as deep learning, Gaussian processes, random forests, support vector machines and boosting.

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Produktdetails

• Verlag: Cambridge University Press
• New ed
• Seitenzahl: 350
• Erscheinungstermin: 31. März 2022
• Englisch
• Abmessung: 258mm x 182mm x 21mm
• Gewicht: 908g
• ISBN-13: 9781108843607
• ISBN-10: 1108843603
• Artikelnr.: 62846616

Autorenporträt

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 Linköping University (including studies at ETH Zürich 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.

Inhaltsangabe

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.