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Cover: 9781108843607 | Machine Learning | A First Course for Engineers and Scientists | Buch
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Machine Learning
A First Course for Engineers and Scientists
Buch von Andreas Lindholm (u. a.)
Sprache: Englisch

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Beschreibung
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.
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.
Über den Autor
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.
Inhaltsverzeichnis
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.
Details
Erscheinungsjahr: 2022
Fachbereich: Kommunikationswissenschaften
Genre: Medienwissenschaften
Rubrik: Wissenschaften
Medium: Buch
Inhalt: Gebunden
ISBN-13: 9781108843607
ISBN-10: 1108843603
Sprache: Englisch
Einband: Gebunden
Autor: Lindholm, Andreas
Wahlström, Niklas
Lindsten, Fredrik
Schön, Thomas B.
Auflage: New ed
Hersteller: Cambridge University Pr.
Abbildungen: Worked examples or Exercises
Maße: 258 x 182 x 21 mm
Von/Mit: Andreas Lindholm (u. a.)
Erscheinungsdatum: 31.03.2022
Gewicht: 0,876 kg
Artikel-ID: 120756007
Über den Autor
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.
Inhaltsverzeichnis
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.
Details
Erscheinungsjahr: 2022
Fachbereich: Kommunikationswissenschaften
Genre: Medienwissenschaften
Rubrik: Wissenschaften
Medium: Buch
Inhalt: Gebunden
ISBN-13: 9781108843607
ISBN-10: 1108843603
Sprache: Englisch
Einband: Gebunden
Autor: Lindholm, Andreas
Wahlström, Niklas
Lindsten, Fredrik
Schön, Thomas B.
Auflage: New ed
Hersteller: Cambridge University Pr.
Abbildungen: Worked examples or Exercises
Maße: 258 x 182 x 21 mm
Von/Mit: Andreas Lindholm (u. a.)
Erscheinungsdatum: 31.03.2022
Gewicht: 0,876 kg
Artikel-ID: 120756007
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