Mathematics for Machine Learning
Mathematics for Machine Learning
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Mathematics for Machine Learning
Mathematics for Machine Learning
This self-contained textbook bridges the gap between mathematical and machine learning texts by introducing mathematical concepts with a minimum of prerequisites.
Mathematics for Machine Learning
Artículo nº.: 192089962

Mathematics for Machine Learning

89% de los encuestados recomendarían esto a un amigo

Artículo nº.: 192089962

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This self-contained textbook bridges the gap between mathematical and machine learning texts by introducing mathematical concepts with a minimum of prerequisites.
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Lo que más destaca

Comprehensive Coverage
Covers essential mathematical concepts like linear algebra, calculus, and statistics, tailored for machine learning applications, ensuring learners grasp foundational knowledge needed for algorithm implementation.
Practical Applications
Integrates real-world examples and practical exercises, bridging the gap between theory and practice, helping learners apply mathematical concepts directly to machine learning problems.
Accessible Learning
Designed for a broad audience, from beginners to advanced practitioners, featuring clear explanations and step-by-step guidance, making complex topics accessible without a heavy mathematical background.

Detalles del producto

Shop Mathematics for Machine Learning online at a best price in Aruba. 110845514X
  • The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.
Publisher Cambridge University Press
Publication date April 23, 2020
Language English
Print length 390 pages
ISBN-10 110845514X
ISBN-13 978-1108455145
Item Weight 800 g
Dimensions 7 x 0.88 x 10 inches (17.8 x 2.2 x 25.4 cm)

¿Quién debería comprarlo?

Suitable For
  • Aspiring Data Scientists

    Ideal for individuals looking to understand the mathematical foundations crucial for data science and machine learning applications.

  • Computer Science Students

    Beneficial for students wanting to enhance their programming skills with essential mathematical concepts for advanced studies in AI.

  • Industry Professionals

    Useful for professionals seeking to upskill in machine learning, providing necessary mathematical knowledge for practical implementations.

Not Suitable For
  • Complete Mathematics Novices

    Not suitable for those without basic mathematical knowledge, as it presumes understanding of fundamental concepts.

DESCRIPCIÓN DEL PRODUCTO

About This Item

Introducing the Mathematics for Machine Learning 1st Edition As the field of machine learning continues to revolutionize various industries, it is essential to have a solid understanding of the mathematical concepts that underpin this powerful technology. The Mathematics for Machine Learning 1st Edition is a comprehensive textbook that covers all the key mathematical foundations needed for successful implementation and application of machine learning algorithms. With endorsements from esteemed experts in the field, such as Joelle Pineau from McGill University and Christopher Bishop from Microsoft Research Cambridge, this book comes highly recommended for both beginners and experienced machine learning researchers and engineers. This self-contained textbook is designed to be accessible to a wide range of readers, with a minimum of prerequisites. It starts with a thorough introduction to linear algebra, which serves as the basis for many machine learning techniques.

From there, it delves into topics such as analytic geometry, matrix decompositions, vector calculus, optimization, probability, and statistics – all of which are crucial for developing a strong understanding of machine learning algorithms. Whether you are a student, a colleague, or simply someone interested in building a solid foundation in machine learning, this book will be an invaluable resource. It presents the necessary mathematical concepts in a clear and concise manner, making it easy to grasp complex ideas and apply them to real-world scenarios. The Mathematics for Machine Learning 1st Edition is not just a tutorial; it is a comprehensive reference text that you can turn to time and time again. It will help you gain a deeper understanding of the mathematical principles behind machine learning algorithms, enabling you to unlock the full potential of this transformative technology. Don't miss out on this essential resource for anyone interested in machine learning.

Order your copy of the Mathematics for Machine Learning 1st Edition today and take your understanding of this exciting field to new heights.

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Preguntas y respuestas de los clientes

  • Pregunta: What are the central machine learning methods discussed in the book?

    Respuesta: Linear regression, principal component analysis, Gaussian mixture models and support vector machines.
  • Pregunta: Are there prerequisites needed for understanding the mathematical concepts?

    Respuesta: No, the book introduces mathematical concepts with a minimum of prerequisites.
  • Pregunta: Where can programming tutorials be accessed?

    Respuesta: Programming tutorials are offered on the book's web site.

English Edition Marc Peter Deisenroth Format: Paperback Applied Editorial Review

Mathematics for Machine Learning is a comprehensive resource published by Cambridge University Press on April 23, 2020. With a 390-page print length, this book provides an in-depth analysis of the mathematical principles important in machine learning. Written in English, it effectively bridges the gap between mathematics and practical machine learning applications. The dimensions of the book are 7 x 0.88 x 10 inches, making it a suitable reference for both students and professionals in the field. Although there are no reviews available, the content is structured to cater to a wide range of readers interested in enhancing their mathematical foundations for machine learning.

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Ventajas

  • Comprehensive coverage of essential mathematical concepts
  • Well-structured for machine learning applications
  • Published by a reputable academic press
  • Appropriate for both students and professionals
  • Clear explanations of complex topics

Desventajas

  • No available user reviews to gauge personal experiences

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AWG 141

AWG 150

Haz tu pedido ahora y recíbelo el Viernes, Octubre 09

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Características y beneficios

  • Fundamental mathematical tools needed to understand machine learning
  • Self-contained textbook for building intuition and practical experience
  • Bridges gap between mathematical and machine learning texts
  • Introduces mathematical concepts with a minimum of prerequisites
  • Chapter exercises and programming tutorials available on the book's website

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