Pattern Recognition and Machine Learning

by Christopher M. Bishop
Pattern Recognition and Machine Learning cover
Good Books rating 4.5
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Waterstones
Hardback • £74.99
Amazon
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Technical
  • ID: 5228
  • Added: 2025-10-22
  • Updated: 2025-10-24
  • Formats: 1
  • Reviews: 3
Reviews
goodreads.com · Unknown · 2025-10-23
excellent 4.50

The book is highly praised for its comprehensive coverage of machine learning and pattern recognition. It is considered a must-read for anyone interested in the field, with clear explanations and practical examples.

This book is widely regarded as a seminal work in the field of machine learning and pattern recognition. Readers appreciate its thorough and accessible approach, making complex topics understandable. The inclusion of practical examples and exercises further enhances the learning experience. Many find it to be an essential resource that bridges the gap between theory and application. The book's clarity and depth make it suitable for both beginners and advanced learners, providing a solid foundation in the subject matter. Overall, it is seen as a valuable addition to any serious student's library.

Quick quotes

    The book is a must-read for anyone interested in machine learning

    It provides a clear and comprehensive introduction to the field

    The practical examples and exercises are particularly helpful.

news.ycombinator.com · Unknown · 2023-07-10
excellent 4.50

The book is highly praised for its comprehensive coverage of pattern recognition and machine learning, making it a valuable resource for both students and professionals. The reviewer appreciates the clear explanations and practical examples provided, which enhance understanding of complex topics.

The book is widely regarded as an essential resource for anyone interested in pattern recognition and machine learning. It covers a broad range of topics with clarity and depth, making it accessible to both beginners and advanced readers. The reviewer particularly appreciates the practical examples and exercises that help reinforce theoretical concepts. The book's structured approach and thorough explanations make it a standout in the field, providing readers with a solid foundation in the subject matter. Overall, it is a highly recommended text for anyone looking to deepen their understanding of these critical areas in computer science.

Quick quotes

    The book is a valuable resource for both students and professionals.

    It covers a broad range of topics with clarity and depth.

    The practical examples and exercises help reinforce theoretical concepts.

ai.stackexchange.com · Unknown · 2022-08-13
excellent 4.50

The reviewer highly recommends 'Applied Predictive Modeling' by Max Kuhn and Kjell Johnson for its rigorous analysis, clear prose, and thorough case studies. The book is praised for its statistical grounding, use of R, and practical approach to predictive modeling.

The reviewer finds 'Applied Predictive Modeling' to be an exceptional resource for anyone serious about predictive modeling. The book is commended for its rigorous analysis, clear writing, and practical approach. The authors emphasize the importance of accurate predictions and provide thorough case studies that illustrate the modeling process. The use of R is integrated throughout the book, making it a valuable tool for practitioners. The reviewer appreciates the book's statistical grounding and the way it encourages readers to question and evaluate results. The book is also noted for its use of forward referencing, which helps readers track topics through the text. Overall, the reviewer highly recommends this book for its depth and practical insights.

Quick quotes

    The authors begin their book by stating that “the practice of predictive modeling defines the process of developing a model in a way that we can understand and quantify the model’s prediction accuracy on future, yet-to-be-seen data”.

    Kuhn and Johnson are intense but not oppressive. They come across like coaches who really, really want you to be able to do this stuff.

    The real statistical value of the text, however, is embedded in the Kuhn and Johnson’s methodology. They take great care to examine the consequences of modeling decisions and continually encourage the reader to challenge the results of particular models.

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