Rogers Simon Mark Girolami (35 results)

Condition: 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.

Condition: as new. Wie neu/Like new.

Condition: 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 Any access codes or passwords originally included with the book may be expired, used or no longer valid.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.

Condition: Very Good. Item in very good condition! Textbooks may not include supplemental items i.e. CDs, access codes etc.

Soft cover. Condition: New. ISBN:9781032937908,Territorial restriction maybe printed on the book. This is an Int'l edition, ISBN and cover may differ from US edition, Contents same as US edition.

Condition: As New. Unread book in perfect condition.

Condition: New.

Condition: New. pp. 427.

Condition: New.

Paperback. Condition: 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. Condition: 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. Condition: New.

Condition: New. In.

Condition: New. pp. 427.

Condition: As New. Unread book in perfect condition.

Condition: New.

Condition: New. pp. 427.

Condition: New. Brand New. Soft Cover International Edition. Different ISBN and Cover Image. Priced lower than the standard editions which is usually intended to make them more affordable for students abroad. The core content of the book is generally the same as the standard edition. The country selling restrictions may be printed on the book but is no problem for the self-use. This Item maybe shipped from US or any other country as we have multiple locations worldwide.

Condition: Brand New. New.SoftCover International edition. Different ISBN and Cover image but contents are same as US edition.Expediting shipping for all USA and Europe orders excluding PO Box. Excellent Customer Service.

Condition: As New. Unread book in perfect condition.

Condition: New. Brand New, Softcover edition. This item may ship from the US or our Overseas warehouse depending on your location and stock availability.

Condition: New. pp. 400.

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

Condition: As New. Unread book in perfect condition.

Book/CD-ROM. Condition: Brand New. 2nd har/psc edition. 440 pages. 10.00x7.00x1.00 inches. In Stock.

Condition: 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. Condition: 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.

Condition: New.

Condition: New. pp. 400.

Condition: New. pp. 400.