First Course Machine Learning (82 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.

hardcover. Condition: Very Good. Cover and edges may have some wear.

paperback. Condition: 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. Condition: New.

Condition: New. 2nd edition NO-PA16APR2015-KAP.

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 Any access codes or passwords originally included with the book may be expired, used or no longer valid.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

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: 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.

Condition: New.

Condition: 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.

Condition: Brand New. New. US edition. Expediting shipping for all USA and Europe orders excluding PO Box. Excellent Customer Service.

Condition: As New. Unread book in perfect condition.

hardcover. Condition: New. New. Ships in a BOX from Central Missouri! UPS shipping for most packages, (Priority Mail for AK/HI/APO/PO Boxes).

Condition: New.

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

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

Hardcover. Condition: 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.

Condition: NEW.

Condition: As New. Unread book in perfect condition.

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. 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.

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.

paperback. Condition: New.

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

Condition: New. In.

Paperback. Condition: Very Good. The book has been read, but is in excellent condition. Pages are intact and not marred by notes or highlighting. The spine remains undamaged.