ISBN: 9781466583283 - machine learning: an algorithmic perspective, second edition (39 risultati)

Hardcover. Condizione: Good. Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority.

Hardcover. Condizione: Acceptable. Connecting readers with great books since 1972. Used textbooks may not include companion materials such as access codes, etc. May have condition issues including wear and notes/highlighting. We ship orders daily and Customer Service is our top priority.

Condizione: Good. Former library copy. Pages intact with minimal writing/highlighting. The binding may be loose and creased. Dust jackets/supplements are not included. Includes library markings. Stock photo provided. Product includes identifying sticker. Better World Books: Buy Books. Do Good.

Hardcover. Condizione: Fair. 2. The item might be beaten up but readable. May contain markings or highlighting, as well as stains, bent corners, or any other major defect, but the text is not obscured in any way.

hardcover. Condizione: Acceptable. Pages include notes, underlining, or highlighting.

Hardback. Condizione: Good. A Proven, Hands-On Approach for Students without a Strong Statistical FoundationSince the best-selling first edition was published, there have been several prominent developments in the field of machine learning, including the increasing work on the statistical interpretations of machine learning algorithms. Unfortunately, computer science students without a strong statistical background often find it hard to get started in this area. Remedying this deficiency, Machine Learning: An Algorithmic Perspective, Second Edition helps students understand the algorithms of machine learning. It puts them on a path toward mastering the relevant mathematics and statistics as well as the necessary programming and experimentation.New to the Second EditionTwo new chapters on deep belief networks and Gaussian processes Reorganization of the chapters to make a more natural flow of contentRevision of the support vector machine material, including a simple implementation for experimentsNew material on random forests, the perceptron convergence theorem, accuracy methods, and conjugate gradient optimization for the multi-layer perceptronAdditional discussions of the Kalman and particle filtersImproved code, including better use of naming conventions in PythonSuitable for both an introductory one-semester course and more advanced courses, the text strongly encourages students to practice with the code. Each chapter includes detailed examples along with further reading and problems. All of the code used to create the examples is available on the author?s website.…

hardcover. Condizione: Very Good. Connecting readers with great books since 1972! Used books may not include companion materials, and may have some shelf wear or limited writing. We ship orders daily and Customer Service is our top priority.

Hardback. Condizione: Good. A Proven, Hands-On Approach for Students without a Strong Statistical FoundationSince the best-selling first edition was published, there have been several prominent developments in the field of machine learning, including the increasing work on the statistical interpretations of machine learning algorithms. Unfortunately, computer science students without a strong statistical background often find it hard to get started in this area. Remedying this deficiency, Machine Learning: An Algorithmic Perspective, Second Edition helps students understand the algorithms of machine learning. It puts them on a path toward mastering the relevant mathematics and statistics as well as the necessary programming and experimentation.New to the Second EditionTwo new chapters on deep belief networks and Gaussian processes Reorganization of the chapters to make a more natural flow of contentRevision of the support vector machine material, including a simple implementation for experimentsNew material on random forests, the perceptron convergence theorem, accuracy methods, and conjugate gradient optimization for the multi-layer perceptronAdditional discussions of the Kalman and particle filtersImproved code, including better use of naming conventions in PythonSuitable for both an introductory one-semester course and more advanced courses, the text strongly encourages students to practice with the code. Each chapter includes detailed examples along with further reading and problems. All of the code used to create the examples is available on the author?s website.…

Hardcover. Condizione: very good. Used items may not include media like access codes or CDs. Fast shipping! Expedited orders take 1-3 business days! Media mail may take up to 5 business days.

hardcover. Condizione: Good. 2nd Edition. Ships in a BOX from Central Missouri! May not include working access code. Will not include dust jacket. Has used sticker(s) and some writing or highlighting. UPS shipping for most packages, (Priority Mail for AK/HI/APO/PO Boxes).

Hardcover, 437 pages. Condizione: Very good. Light rubbing to extremities of glossy pictorial boards. Head of spine slightly bumped. Pages appear free of writing / highlighting. In nice shape.

Hardcover. Condizione: new. New Copy. Customer Service Guaranteed.

Condizione: good. May show signs of wear, highlighting, writing, and previous use. This item may be a former library book with typical markings. No guarantee on products that contain supplements Your satisfaction is 100% guaranteed. Twenty-five year bookseller with shipments to over fifty million happy customers.…

HRD. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

Condizione: New.

Hardcover. Condizione: New. Brand new book, sourced directly from publisher. Dispatch time is 24-48 hours from our warehouse. Book will be sent in robust, secure packaging to ensure it reaches you securely.

Condizione: As New. Unread book in perfect condition.

Condizione: New.

Condizione: very good. Le livre peut montrer des signes d'usure dus a une utilisation constante, etre marque, porter des marques d'identification ou presenter plusieurs dommages esthetiques mineurs. vendeur professionnel; envoi soigne en 24/48h.

Condizione: New. Num Pages: 457 pages, 205 black & white illustrations, 21 black & white tables. BIC Classification: UYQM. Category: (UU) Undergraduate. Dimension: 262 x 183 x 24. Weight in Grams: 1022. . 2014. 2nd Edition. Hardcover. . . . .

Hardcover. Condizione: new. Hardcover. A Proven, Hands-On Approach for Students without a Strong Statistical FoundationSince the best-selling first edition was published, there have been several prominent developments in the field of machine learning, including the increasing work on the statistical interpretations of machine learning algorithms. Unfortunately, computer science students without a strong statistical background often find it hard to get started in this area. Remedying this deficiency, Machine Learning: An Algorithmic Perspective, Second Edition helps students understand the algorithms of machine learning. It puts them on a path toward mastering the relevant mathematics and statistics as well as the necessary programming and experimentation.New to the Second EditionTwo new chapters on deep belief networks and Gaussian processes Reorganization of the chapters to make a more natural flow of contentRevision of the support vector machine material, including a simple implementation for experimentsNew material on random forests, the perceptron convergence theorem, accuracy methods, and conjugate gradient optimization for the multi-layer perceptronAdditional discussions of the Kalman and particle filtersImproved code, including better use of naming conventions in PythonSuitable for both an introductory one-semester course and more advanced courses, the text strongly encourages students to practice with the code. Each chapter includes detailed examples along with further reading and problems. All of the code used to create the examples is available on the authors website. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Hardback. Condizione: New. A Proven, Hands-On Approach for Students without a Strong Statistical FoundationSince the best-selling first edition was published, there have been several prominent developments in the field of machine learning, including the increasing work on the statistical interpretations of machine learning algorithms. Unfortunately, computer science students without a strong statistical background often find it hard to get started in this area. Remedying this deficiency, Machine Learning: An Algorithmic Perspective, Second Edition helps students understand the algorithms of machine learning. It puts them on a path toward mastering the relevant mathematics and statistics as well as the necessary programming and experimentation.New to the Second EditionTwo new chapters on deep belief networks and Gaussian processes Reorganization of the chapters to make a more natural flow of contentRevision of the support vector machine material, including a simple implementation for experimentsNew material on random forests, the perceptron convergence theorem, accuracy methods, and conjugate gradient optimization for the multi-layer perceptronAdditional discussions of the Kalman and particle filtersImproved code, including better use of naming conventions in PythonSuitable for both an introductory one-semester course and more advanced courses, the text strongly encourages students to practice with the code. Each chapter includes detailed examples along with further reading and problems. All of the code used to create the examples is available on the author's website.…

Condizione: As New. Unread book in perfect condition.

Condizione: New. In English.

Condizione: good. May show signs of wear, highlighting, writing, and previous use. This item may be a former library book with typical markings. No guarantee on products that contain supplements Your satisfaction is 100% guaranteed. Twenty-five year bookseller with shipments to over fifty million happy customers.…

Condizione: NEW.

Hardcover. Condizione: Brand New. 2nd har/psc edition. 437 pages. 10.00x7.00x1.25 inches. In Stock.

Hardback. Condizione: New. A Proven, Hands-On Approach for Students without a Strong Statistical FoundationSince the best-selling first edition was published, there have been several prominent developments in the field of machine learning, including the increasing work on the statistical interpretations of machine learning algorithms. Unfortunately, computer science students without a strong statistical background often find it hard to get started in this area. Remedying this deficiency, Machine Learning: An Algorithmic Perspective, Second Edition helps students understand the algorithms of machine learning. It puts them on a path toward mastering the relevant mathematics and statistics as well as the necessary programming and experimentation.New to the Second EditionTwo new chapters on deep belief networks and Gaussian processes Reorganization of the chapters to make a more natural flow of contentRevision of the support vector machine material, including a simple implementation for experimentsNew material on random forests, the perceptron convergence theorem, accuracy methods, and conjugate gradient optimization for the multi-layer perceptronAdditional discussions of the Kalman and particle filtersImproved code, including better use of naming conventions in PythonSuitable for both an introductory one-semester course and more advanced courses, the text strongly encourages students to practice with the code. Each chapter includes detailed examples along with further reading and problems. All of the code used to create the examples is available on the author's website.…

Condizione: New. Num Pages: 457 pages, 205 black & white illustrations, 21 black & white tables. BIC Classification: UYQM. Category: (UU) Undergraduate. Dimension: 262 x 183 x 24. Weight in Grams: 1022. . 2014. 2nd Edition. Hardcover. . . . . Books ship from the US and Ireland.

Hardback. Condizione: New. New copy - Usually dispatched within 3 working days.