9781108843607 - machine learning: a first course for engineers and scientists di lindholm, andreas; wahlström, niklas; lindsten, fredrik; schön, thomas b. (36 risultati)
Machine Learning: A First Course for Engineers and Scientists
Lindholm, Andreas,Wahlstrà m, Niklas,Lindsten, Fredrik,Schà n, Thomas B.
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Da: Books From California, Simi Valley, CA, U.S.A.Books From California
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hardcover. Condizione: Very Good. Cover and edges may have some wear.
Machine Learning: A First Course for Engineers and Scientists
Lindholm, Andreas; Wahlström, Niklas; Lindsten, Fredrik; Schön, Thomas B.
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Da: Romtrade Corp., STERLING HEIGHTS, MI, U.S.A.Romtrade Corp.
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Condizione: New. This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide.
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Da: Basi6 International, Irving, TX, U.S.A.Basi6 International
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Condizione: Brand New. New. US edition. Expediting shipping for all USA and Europe orders excluding PO Box. Excellent Customer Service.
Machine Learning : A First Course for Engineers and Scientists
Lindholm, Andreas; Wahlström, Niklas; Lindsten, Fredrik; Schön, Thomas B.
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Da: GreatBookPrices, Columbia, MD, U.S.A.GreatBookPrices
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Condizione: New.
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Da: Textbooks_Source, Columbia, MO, U.S.A.Textbooks_Source
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EUR 59,25
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hardcover. Condizione: New. New. Ships in a BOX from Central Missouri! UPS shipping for most packages, (Priority Mail for AK/HI/APO/PO Boxes).
Machine Learning: A First Course for Engineers and Scientists
Lindholm, Andreas; Wahlström, Niklas; Lindsten, Fredrik; Schön, Thomas B.
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- Prima edizione
Da: Prior Books Ltd, Cheltenham, , Regno UnitoPrior Books Ltd
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EUR 38,81
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Hardcover. Condizione: Like New. First Edition. Hardback book in nearly new condition: firm and square with strong joints. Just a few hardly noticeable rubs or very mild bumps. Hence a non-text page shows a small 'damaged' stamp. Despite such this book looks and feels unread. Thus the contents are crisp, fresh and tight. And so…a very nice book in great condition, now offered for sale at a reasonable price.
Machine Learning : A First Course for Engineers and Scientists
Lindholm, Andreas; Wahlström, Niklas; Lindsten, Fredrik; Schön, Thomas B.
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Da: GreatBookPrices, Columbia, MD, U.S.A.GreatBookPrices
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Condizione: As New. Unread book in perfect condition.
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Da: Books Puddle, New York, NY, U.S.A.Books Puddle
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Condizione: Used. pp. 350 New edition niversity Press.
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Da: Majestic Books, Hounslow, , Regno UnitoMajestic Books
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Condizione: Used. pp. 350.
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Da: PBShop.store US, Wood Dale, IL, U.S.A.PBShop.store US
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HRD. Condizione: New. New Book. Shipped from UK. Established seller since 2000.
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Da: PBShop.store UK, Fairford, GLOS, Regno UnitoPBShop.store UK
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HRD. Condizione: New. New Book. Shipped from UK. Established seller since 2000.
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Da: Biblios, frankfurt am main, HESSE, GermaniaBiblios
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Condizione: Used. pp. 350.
Machine Learning: A First Course for Engineers and Scientists
Lindholm, Andreas; Wahlström, Niklas; Lindsten, Fredrik; Schön, Thomas B.
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Da: California Books, Miami, FL, U.S.A.California Books
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Condizione: New.
Machine Learning : A First Course for Engineers and Scientists
Lindholm, Andreas; Wahlström, Niklas; Lindsten, Fredrik; Schön, Thomas B.
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Da: GreatBookPricesUK, Woodford Green, Regno UnitoGreatBookPricesUK
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Condizione: New.
Machine Learning: A First Course for Engineers and Scientists
Andreas Lindholm,Niklas Wahlstr�m,Fredrik Lindsten,Thomas B. Sch�n
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Da: Chiron Media, Wallingford, , Regno UnitoChiron Media
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EUR 66,81
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Hardcover. Condizione: New.
Machine Learning: A First Course for Engineers and Scientists
Lindholm, Andreas; Wahlström, Niklas; Lindsten, Fredrik; Schön, Thomas B.
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Da: Ria Christie Collections, Uxbridge, Regno UnitoRia Christie Collections
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EUR 72,89
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Condizione: New. In.
Machine Learning: A First Course for Engineers and Scientists
Andreas Lindholm,Niklas Wahlstr�m,Fredrik Lindsten,Thomas B. Sch�n
- Rilegato
Da: Chiron Media, Wallingford, , Regno UnitoChiron Media
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EUR 71,48
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Hardcover. Condizione: New.
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Da: Grand Eagle Retail, Bensenville, IL, U.S.A.Grand Eagle Retail
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EUR 91,20
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Hardcover. Condizione: new. Hardcover. 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 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Da: Kennys Bookshop and Art Galleries Ltd., Galway, GY, IrlandaKennys Bookshop and Art Galleries Ltd.
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EUR 79,81
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Condizione: New. 2022. New. Hardcover. . . . . .
Machine Learning : A First Course for Engineers and Scientists
Lindholm, Andreas; Wahlström, Niklas; Lindsten, Fredrik; Schön, Thomas B.
- Rilegato
Da: GreatBookPricesUK, Woodford Green, Regno UnitoGreatBookPricesUK
Contatta il venditoreVenditore con 5 stelleCondizione: Usato - Come nuovo
EUR 76,24
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Condizione: As New. Unread book in perfect condition.
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Da: Rheinberg-Buch Andreas Meier eK, Bergisch Gladbach, , GermaniaRheinberg-Buch Andreas Meier eK
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EUR 68,50
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Buch. Condizione: Neu. Neuware -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 lo…gistic 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. 338 pp. Englisch.
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Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, , GermaniaBuchWeltWeit Ludwig Meier e.K.
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EUR 68,50
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Buch. Condizione: Neu. Neuware -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 lo…gistic 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. 338 pp. Englisch.
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Da: Wegmann1855, Zwiesel, , GermaniaWegmann1855
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EUR 68,50
EUR 25,95 spedizioneSpedito da Germania a U.S.A.Quantità: 1 disponibili
Buch. Condizione: Neu. Neuware -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 lo…gistic 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.
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Da: Rarewaves.com USA, London, LONDO, Regno UnitoRarewaves.com USA
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EUR 102,76
Spedizione gratuitaSpedito da Regno Unito a U.S.A.Quantità: 1 disponibili
Hardback. Condizione: New. 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 logisti…c 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.
- Rilegato
Da: Kennys Bookstore, Olney, MD, U.S.A.Kennys Bookstore
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EUR 101,68
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Condizione: New. 2022. New. Hardcover. . . . . . Books ship from the US and Ireland.
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Da: moluna, Greven, , Germaniamoluna
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EUR 62,52
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Condizione: New. 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 d.
Machine Learning: A First Course for Engineers and Scientists
Lindholm, Andreas/ Wahlström, Niklas/ Lindsten, Fredrik/ Schön, Thomas B.
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Da: Revaluation Books, Exeter, , Regno UnitoRevaluation Books
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EUR 105,23
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Hardcover. Condizione: Brand New. 325 pages. 10.20x7.20x0.80 inches. In Stock.
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Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germaniabuchversandmimpf2000
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EUR 68,50
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Buch. Condizione: Neu. Neuware -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 lo…gistic 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.Libri GmbH, Europaallee 1, 36244 Bad Hersfeld 338 pp. Englisch.
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Da: preigu, Osnabrück, Germaniapreigu
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EUR 63,60
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Buch. Condizione: Neu. Machine Learning | A First Course for Engineers and Scientists | Andreas Lindholm (u. a.) | Buch | Gebunden | Englisch | 2022 | Cambridge University Pr. | EAN 9781108843607 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu.
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Da: AHA-BUCH GmbH, Einbeck, GermaniaAHA-BUCH GmbH
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 76,05
EUR 64,29 spedizioneSpedito da Germania a U.S.A.Quantità: 2 disponibili
Buch. Condizione: Neu. Neuware - 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 l…ogistic 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.
