ISBN: 9780367574642 - a first course in machine learning (chapman & hall/crc machine learning & pattern recognition) (33 resultados)

Condición: acceptable. Used - Acceptable: All pages and the cover are intact, but shrink wrap, dust covers, or boxed set case may be missing. Pages may include limited notes, highlighting, or minor water damage but the text is readable. Pages may include limited notes and highlighting, but the text cannot be obscured or unreadable. Any access codes or passwords originally included with the book may be expired, used or no longer valid. Image is stock photo and cover art edition may be different than pictured.…

Condición: Good. This is an ex-library book and may have the usual library/used-book markings inside.This book has soft covers. In good all round condition. Please note the Image in this listing is a stock photo and may not match the covers of the actual item,750grams, ISBN:9780367574642.

Condición: New.

PAP. Condición: New. New Book. Shipped from UK. Established seller since 2000.

Paperback. Condición: New. Brand new book, sourced directly from publisher. Dispatch time is 7-8 days from our warehouse. Book will be sent in robust, secure packaging to ensure it reaches you securely.

Condición: very good. Gut/Very good: Buch bzw. Schutzumschlag mit wenigen Gebrauchsspuren an Einband, Schutzumschlag oder Seiten. / Describes a book or dust jacket that does show some signs of wear on either the binding, dust jacket or pages.

Condición: As New. Unread book in perfect condition.

PAP. Condición: New. New Book. Shipped from UK. Established seller since 2000.

paperback. Condición: 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.

Paperback. Condición: New. 2nd. "A First Course in Machine Learning by Simon Rogers and Mark Girolami is the best introductory book for ML currently available. It combines rigor and precision with accessibility, starts from a detailed explanation of the basic foundations of Bayesian analysis in the simplest of settings, and goes all the way to the frontiers of the subject such as infinite mixture models, GPs, and MCMC."-Devdatt Dubhashi, Professor, Department of Computer Science and Engineering, Chalmers University, Sweden"This textbook manages to be easier to read than other comparable books in the subject while retaining all the rigorous treatment needed. The new chapters put it at the forefront of the field by covering topics that have become mainstream in machine learning over the last decade."-Daniel Barbara, George Mason University, Fairfax, Virginia, USA"The new edition of A First Course in Machine Learning by Rogers and Girolami is an excellent introduction to the use of statistical methods in machine learning. The book introduces concepts such as mathematical modeling, inference, and prediction, providing 'just in time' the essential background on linear algebra, calculus, and probability theory that the reader needs to understand these concepts."-Daniel Ortiz-Arroyo, Associate Professor, Aalborg University Esbjerg, Denmark"I was impressed by how closely the material aligns with the needs of an introductory course on machine learning, which is its greatest strength.Overall, this is a pragmatic and helpful book, which is well-aligned to the needs of an introductory course and one that I will be looking at for my own students in coming months."-David Clifton, University of Oxford, UK"The first edition of this book was already an excellent introductory text on machine learning for an advanced undergraduate or taught masters level course, or indeed for anybody who wants to learn about an interesting and important field of computer science. The additional chapters of advanced material on Gaussian process, MCMC and mixture modeling provide an ideal basis for practical projects, without disturbing the very clear and readable exposition of the basics contained in the first part of the book." -Gavin Cawley, Senior Lecturer, School of Computing Sciences, University of East Anglia, UK"This book could be used for junior/senior undergraduate students or first-year graduate students, as well as individuals who want to explore the field of machine learning.The book introduces not only the concepts but the underlying ideas on algorithm implementation from a critical thinking perspective."-Guangzhi Qu, Oakland University, Rochester, Michigan, USA.…

Condición: New. pp. 427.

Condición: New.

Condición: New. 2020. 2nd Edition. Paperback. . . . . .

Paperback. Condición: New. 2nd. "A First Course in Machine Learning by Simon Rogers and Mark Girolami is the best introductory book for ML currently available. It combines rigor and precision with accessibility, starts from a detailed explanation of the basic foundations of Bayesian analysis in the simplest of settings, and goes all the way to the frontiers of the subject such as infinite mixture models, GPs, and MCMC."-Devdatt Dubhashi, Professor, Department of Computer Science and Engineering, Chalmers University, Sweden"This textbook manages to be easier to read than other comparable books in the subject while retaining all the rigorous treatment needed. The new chapters put it at the forefront of the field by covering topics that have become mainstream in machine learning over the last decade."-Daniel Barbara, George Mason University, Fairfax, Virginia, USA"The new edition of A First Course in Machine Learning by Rogers and Girolami is an excellent introduction to the use of statistical methods in machine learning. The book introduces concepts such as mathematical modeling, inference, and prediction, providing 'just in time' the essential background on linear algebra, calculus, and probability theory that the reader needs to understand these concepts."-Daniel Ortiz-Arroyo, Associate Professor, Aalborg University Esbjerg, Denmark"I was impressed by how closely the material aligns with the needs of an introductory course on machine learning, which is its greatest strength.Overall, this is a pragmatic and helpful book, which is well-aligned to the needs of an introductory course and one that I will be looking at for my own students in coming months."-David Clifton, University of Oxford, UK"The first edition of this book was already an excellent introductory text on machine learning for an advanced undergraduate or taught masters level course, or indeed for anybody who wants to learn about an interesting and important field of computer science. The additional chapters of advanced material on Gaussian process, MCMC and mixture modeling provide an ideal basis for practical projects, without disturbing the very clear and readable exposition of the basics contained in the first part of the book." -Gavin Cawley, Senior Lecturer, School of Computing Sciences, University of East Anglia, UK"This book could be used for junior/senior undergraduate students or first-year graduate students, as well as individuals who want to explore the field of machine learning.The book introduces not only the concepts but the underlying ideas on algorithm implementation from a critical thinking perspective."-Guangzhi Qu, Oakland University, Rochester, Michigan, USA.…

Paperback. Condición: new. Paperback. "A First Course in Machine Learning by Simon Rogers and Mark Girolami is the best introductory book for ML currently available. It combines rigor and precision with accessibility, starts from a detailed explanation of the basic foundations of Bayesian analysis in the simplest of settings, and goes all the way to the frontiers of the subject such as infinite mixture models, GPs, and MCMC."Devdatt Dubhashi, Professor, Department of Computer Science and Engineering, Chalmers University, Sweden"This textbook manages to be easier to read than other comparable books in the subject while retaining all the rigorous treatment needed. The new chapters put it at the forefront of the field by covering topics that have become mainstream in machine learning over the last decade."Daniel Barbara, George Mason University, Fairfax, Virginia, USA"The new edition of A First Course in Machine Learning by Rogers and Girolami is an excellent introduction to the use of statistical methods in machine learning. The book introduces concepts such as mathematical modeling, inference, and prediction, providing just in time the essential background on linear algebra, calculus, and probability theory that the reader needs to understand these concepts."Daniel Ortiz-Arroyo, Associate Professor, Aalborg University Esbjerg, Denmark"I was impressed by how closely the material aligns with the needs of an introductory course on machine learning, which is its greatest strengthOverall, this is a pragmatic and helpful book, which is well-aligned to the needs of an introductory course and one that I will be looking at for my own students in coming months."David Clifton, University of Oxford, UK"The first edition of this book was already an excellent introductory text on machine learning for an advanced undergraduate or taught masters level course, or indeed for anybody who wants to learn about an interesting and important field of computer science. The additional chapters of advanced material on Gaussian process, MCMC and mixture modeling provide an ideal basis for practical projects, without disturbing the very clear and readable exposition of the basics contained in the first part of the book." Gavin Cawley, Senior Lecturer, School of Computing Sciences, University of East Anglia, UK"This book could be used for junior/senior undergraduate students or first-year graduate students, as well as individuals who want to explore the field of machine learningThe book introduces not only the concepts but the underlying ideas on algorithm implementation from a critical thinking perspective."Guangzhi Qu, Oakland University, Rochester, Michigan, USA The new edition of this popular, undergraduate textbook has been revised and updated to reflect current growth areas in Machine Learning. The new edition includes three new chapters with more detailed discussion of Markov Chain Monte Carlo techniques, Classification and Regression with Gaussian Processes, and Dirichlet Process models. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Condición: New.

Condición: New. pp. 427.

Condición: New. In English.

Condición: As New. Unread book in perfect condition.

Paperback / softback. Condición: New. New copy - Usually dispatched within 4 working days.

Condición: New. pp. 427.

Condición: New. 2020. 2nd Edition. Paperback. . . . . . Books ship from the US and Ireland.

Condición: NEW.

Paperback. Condición: Brand New. 2nd edition. 397 pages. 9.25x6.25x1.00 inches. In Stock.

Kartoniert / Broschiert. Condición: New. Simon Rogers is a lecturer in the School of Computing Science at the University of Glasgow, where he teaches a masters-level machine learning course on which this book is based. Dr. Rogers is an active researcher in machine learning, par.

Paperback. Condición: New. 2nd. "A First Course in Machine Learning by Simon Rogers and Mark Girolami is the best introductory book for ML currently available. It combines rigor and precision with accessibility, starts from a detailed explanation of the basic foundations of Bayesian analysis in the simplest of settings, and goes all the way to the frontiers of the subject such as infinite mixture models, GPs, and MCMC."-Devdatt Dubhashi, Professor, Department of Computer Science and Engineering, Chalmers University, Sweden"This textbook manages to be easier to read than other comparable books in the subject while retaining all the rigorous treatment needed. The new chapters put it at the forefront of the field by covering topics that have become mainstream in machine learning over the last decade."-Daniel Barbara, George Mason University, Fairfax, Virginia, USA"The new edition of A First Course in Machine Learning by Rogers and Girolami is an excellent introduction to the use of statistical methods in machine learning. The book introduces concepts such as mathematical modeling, inference, and prediction, providing 'just in time' the essential background on linear algebra, calculus, and probability theory that the reader needs to understand these concepts."-Daniel Ortiz-Arroyo, Associate Professor, Aalborg University Esbjerg, Denmark"I was impressed by how closely the material aligns with the needs of an introductory course on machine learning, which is its greatest strength.Overall, this is a pragmatic and helpful book, which is well-aligned to the needs of an introductory course and one that I will be looking at for my own students in coming months."-David Clifton, University of Oxford, UK"The first edition of this book was already an excellent introductory text on machine learning for an advanced undergraduate or taught masters level course, or indeed for anybody who wants to learn about an interesting and important field of computer science. The additional chapters of advanced material on Gaussian process, MCMC and mixture modeling provide an ideal basis for practical projects, without disturbing the very clear and readable exposition of the basics contained in the first part of the book." -Gavin Cawley, Senior Lecturer, School of Computing Sciences, University of East Anglia, UK"This book could be used for junior/senior undergraduate students or first-year graduate students, as well as individuals who want to explore the field of machine learning.The book introduces not only the concepts but the underlying ideas on algorithm implementation from a critical thinking perspective."-Guangzhi Qu, Oakland University, Rochester, Michigan, USA.…

Taschenbuch. Condición: Neu. A First Course in Machine Learning | Simon Rogers (u. a.) | Taschenbuch | Einband - flex.(Paperback) | Englisch | 2020 | Chapman and Hall/CRC | EAN 9780367574642 | Verantwortliche Person für die EU: Taylor and Francis, Taylor & Francis Verlag Gmbh, Kaufingerstr 24, 80331 München, gpsr[at]taylorandfrancis[dot]com | Anbieter: preigu.…

Paperback. Condición: new. Paperback. "A First Course in Machine Learning by Simon Rogers and Mark Girolami is the best introductory book for ML currently available. It combines rigor and precision with accessibility, starts from a detailed explanation of the basic foundations of Bayesian analysis in the simplest of settings, and goes all the way to the frontiers of the subject such as infinite mixture models, GPs, and MCMC."Devdatt Dubhashi, Professor, Department of Computer Science and Engineering, Chalmers University, Sweden"This textbook manages to be easier to read than other comparable books in the subject while retaining all the rigorous treatment needed. The new chapters put it at the forefront of the field by covering topics that have become mainstream in machine learning over the last decade."Daniel Barbara, George Mason University, Fairfax, Virginia, USA"The new edition of A First Course in Machine Learning by Rogers and Girolami is an excellent introduction to the use of statistical methods in machine learning. The book introduces concepts such as mathematical modeling, inference, and prediction, providing just in time the essential background on linear algebra, calculus, and probability theory that the reader needs to understand these concepts."Daniel Ortiz-Arroyo, Associate Professor, Aalborg University Esbjerg, Denmark"I was impressed by how closely the material aligns with the needs of an introductory course on machine learning, which is its greatest strengthOverall, this is a pragmatic and helpful book, which is well-aligned to the needs of an introductory course and one that I will be looking at for my own students in coming months."David Clifton, University of Oxford, UK"The first edition of this book was already an excellent introductory text on machine learning for an advanced undergraduate or taught masters level course, or indeed for anybody who wants to learn about an interesting and important field of computer science. The additional chapters of advanced material on Gaussian process, MCMC and mixture modeling provide an ideal basis for practical projects, without disturbing the very clear and readable exposition of the basics contained in the first part of the book." Gavin Cawley, Senior Lecturer, School of Computing Sciences, University of East Anglia, UK"This book could be used for junior/senior undergraduate students or first-year graduate students, as well as individuals who want to explore the field of machine learningThe book introduces not only the concepts but the underlying ideas on algorithm implementation from a critical thinking perspective."Guangzhi Qu, Oakland University, Rochester, Michigan, USA The new edition of this popular, undergraduate textbook has been revised and updated to reflect current growth areas in Machine Learning. The new edition includes three new chapters with more detailed discussion of Markov Chain Monte Carlo techniques, Classification and Regression with Gaussian Processes, and Dirichlet Process models. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…

Paperback. Condición: New. 2nd. "A First Course in Machine Learning by Simon Rogers and Mark Girolami is the best introductory book for ML currently available. It combines rigor and precision with accessibility, starts from a detailed explanation of the basic foundations of Bayesian analysis in the simplest of settings, and goes all the way to the frontiers of the subject such as infinite mixture models, GPs, and MCMC."-Devdatt Dubhashi, Professor, Department of Computer Science and Engineering, Chalmers University, Sweden"This textbook manages to be easier to read than other comparable books in the subject while retaining all the rigorous treatment needed. The new chapters put it at the forefront of the field by covering topics that have become mainstream in machine learning over the last decade."-Daniel Barbara, George Mason University, Fairfax, Virginia, USA"The new edition of A First Course in Machine Learning by Rogers and Girolami is an excellent introduction to the use of statistical methods in machine learning. The book introduces concepts such as mathematical modeling, inference, and prediction, providing 'just in time' the essential background on linear algebra, calculus, and probability theory that the reader needs to understand these concepts."-Daniel Ortiz-Arroyo, Associate Professor, Aalborg University Esbjerg, Denmark"I was impressed by how closely the material aligns with the needs of an introductory course on machine learning, which is its greatest strength.Overall, this is a pragmatic and helpful book, which is well-aligned to the needs of an introductory course and one that I will be looking at for my own students in coming months."-David Clifton, University of Oxford, UK"The first edition of this book was already an excellent introductory text on machine learning for an advanced undergraduate or taught masters level course, or indeed for anybody who wants to learn about an interesting and important field of computer science. The additional chapters of advanced material on Gaussian process, MCMC and mixture modeling provide an ideal basis for practical projects, without disturbing the very clear and readable exposition of the basics contained in the first part of the book." -Gavin Cawley, Senior Lecturer, School of Computing Sciences, University of East Anglia, UK"This book could be used for junior/senior undergraduate students or first-year graduate students, as well as individuals who want to explore the field of machine learning.The book introduces not only the concepts but the underlying ideas on algorithm implementation from a critical thinking perspective."-Guangzhi Qu, Oakland University, Rochester, Michigan, USA.…

PF. Condición: New. This item is printed on demand. Brand new book, sourced directly from publisher. Dispatch time is 6-7 days from our warehouse. Book will be sent in robust, secure packaging to ensure it reaches you securely.