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

This comprehensive textbook offers a broad overview of the intersection of data-driven methods, machine learning, applied optimization, and classical fields of engineering mathematics and mathematical physics. Whether you're an advanced undergraduate or beginning graduate student in the engineering or physical sciences, this book will provide you with the necessary knowledge and skills for the next generation of scientific discovery.

• Title: Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control 2nd Edition
• Programming Languages: Python, MATLAB®, Julia, R
• Number of Pages: Varies
• Publisher: Varies
• Supplementary Material: Lecture videos, homeworks, data, and code in MATLAB®, Python, Julia, R (available on databookuw.com)

• Broad overview of data-driven methods, machine learning, and applied optimization
• Integration of dynamical systems modeling and control with modern machine learning methods
• Relevance, simplicity, and generality of covered topics
• Accessible to advanced undergraduate and beginning graduate students
• New chapters on reinforcement learning and physics informed machine learning
• Significant new sections throughout the book
• Chapter exercises for better understanding and practice

• Studying and understanding data-driven methods, machine learning, and applied optimization
• Applying modern machine learning techniques in dynamical systems modeling and control
• Researching or working in the fields of engineering mathematics and mathematical physics
• Supplemental material such as lecture videos and code for self-study or classroom use

• Practical Machine Learning for Computer Vision by Martin Görner et al.
• Deep Learning by Ian Goodfellow et al.

• This textbook provides a comprehensive understanding of data-driven science and engineering. The integration of dynamical systems modeling and control with modern machine learning methods is particularly impressive.
• The inclusion of new chapters on reinforcement learning and physics informed machine learning makes this second edition even more valuable. The chapter exercises and supplementary material are great for practice and self-study.
• As an advanced undergraduate student, I found this book to be accessible and easy to follow. The topics are well-explained and the examples are helpful in understanding the concepts.
• I have been using this book for my research in engineering mathematics and it has been incredibly useful. The authors have done a great job of covering both introductory and advanced material.
• The online supplementary material, including lecture videos and code, is a great addition. It provides additional resources for learning and applying the concepts taught in the book.

• The price of the Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control 2nd Edition is competitive compared to other similar textbooks in the market.
• Considering the wide range of topics covered, the inclusion of new chapters, and the availability of online supplementary material, the price of this textbook offers excellent value for money.
• While the exact price may vary depending on the retailer and edition, it is important to compare prices and consider the additional resources provided.

• Consider your level of expertise and the specific areas of interest you have in data-driven science and engineering
• Evaluate the relevance of the integration between dynamical systems modeling and control with modern machine learning methods
• Assess the accessibility and clarity of the book's explanations and examples
• Take advantage of the online supplementary material available on databookuw.com for enhanced learning and practice

In conclusion, Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control 2nd Edition is a must-have textbook for students and professionals in the engineering and physical sciences. With its comprehensive coverage, integration of key topics, and supplementary material, this book provides a solid foundation for understanding and applying data-driven methods in scientific discovery.