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Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control
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Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control
Data-driven discovery is revolutionizing how we model, predict, and control complex systems. Now with Python and MATLAB®, this textbook trains mathematical scientists and engineers for the next generation of scientific discovery.
Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control
商品＃: 71766102

Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control

89% of respondents would recommend this to a friend

商品＃: 71766102

TWD 2758

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Data-driven discovery is revolutionizing how we model, predict, and control complex systems. Now with Python and MATLAB®, this textbook trains mathematical scientists and engineers for the next generation of scientific discovery.
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What Stands Out

Interdisciplinary Approach
Integrates machine learning with dynamical systems and control, providing a comprehensive framework for tackling complex engineering challenges. This makes it suitable for a wide range of scientific and engineering applications.
Updated Content
The 2nd edition includes the latest advancements in data-driven methods, ensuring learners are equipped with current knowledge and techniques that are highly relevant in today’s rapidly evolving tech landscape.
Practical Applications
Focuses on real-world applications, allowing students and professionals to see the practical implications of theoretical concepts. This enhances understanding and encourages effective problem-solving strategies in engineering.

產品詳情

Find the best deals on the 2nd edition of Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control at Ubuy Taiwan. Global store with worldwide shipping.
Publisher Cambridge University Press
Publication date July 28, 2022
Edition 2nd
Language English
Print length 614 pages
ISBN-10 1009098489
ISBN-13 978-1009098489
Item Weight 3.06 pounds (1.39 kg)
Dimensions 7 x 1.25 x 10 inches (17.8 x 3.2 x 25.4 cm)

Who Should Buy?

Suitable For
  • Graduate Students

    Ideal for graduate students delving into machine learning and engineering, providing a robust theoretical and practical foundation.

  • Research Professionals

    Suitable for researchers focusing on dynamical systems, offering advanced insights and methodologies related to data-driven techniques.

  • Industry Engineers

    Beneficial for engineers in industries applying control systems, integrating machine learning with engineering principles seamlessly.

Not Suitable For
  • Beginner Learners

    Not suitable for complete beginners lacking prior knowledge in machine learning or dynamical systems, as it assumes foundational understanding.

產品描述

Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control

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Control Systems Editorial Review

Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control offers an engaging exploration of advanced concepts like Koopman operators and Sparse Identification of Nonlinear Dynamics (SINDy) for modeling chaotic systems. This second edition from Cambridge University Press, published on July 28, 2022, spans 614 pages, seamlessly integrating mathematical theories with practical Python code examples that enhance the reader's understanding. Readers praise its ability to bridge complex mathematical explanations with real-world application, making challenging principles approachable and applicable. Additionally, while the content and instructional approach receive high acclaim, there are concerns regarding the book’s binding quality, suggesting potential drawbacks for the physical copy.

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優點

  • Comprehensive coverage of advanced machine learning topics
  • Python examples for practical application
  • Excellent pedagogical style complements video lectures
  • Well-structured content for better understanding
  • Ideal for scientists, engineers, and applied mathematicians

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立即訂購並活動它 週四, 十月 15

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特色和優勢

  • Textbook trains mathematical scientists and engineers for the next generation of scientific discovery.
  • Broad overview of the growing intersection of data-driven methods, machine learning, applied optimization, and classical fields of engineering mathematics and mathematical physics.
  • Includes methods that were chosen for their relevance, simplicity, and generality.
  • Suitable for advanced undergraduate and beginning graduate students from the engineering and physical sciences.
  • New chapters on reinforcement learning and physics-informed machine learning, significant new sections throughout, and chapter exercises.
  • Online supplementary material available on databookuw.com.

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