An Introduction to Optimization: With Applications to Machine Learning 4th Edition by Edwin K. P. Chong (Author)

Embark on a journey through the world of optimization with the fourth edition of "An Introduction to Optimization." Authored by Edwin K. P. Chong and Stanislaw H. Zak, this comprehensive guide offers a blend of theoretical foundations and practical applications, focusing on the fundamental principles of optimization and their relevance to machine learning. Whether you're a student, researcher, or practitioner in the fields of mathematics, computer science, or engineering, this book serves as an invaluable resource for understanding the intricate concepts and techniques essential for tackling optimization problems in diverse domains.

The book begins by laying a solid groundwork in optimization theory, covering topics such as mathematical programming, convex optimization, and nonlinear programming. Through clear explanations and illustrative examples, readers gain a deep understanding of optimization algorithms and techniques, including gradient descent, constrained optimization, and metaheuristics. With a focus on both classical and modern approaches, the authors provide insights into the latest advancements in optimization theory and practice, empowering readers to apply optimization principles to real-world problems with confidence.

In the fourth edition, Chong and Zak expand their exploration of optimization's intersection with machine learning, offering a wealth of examples and applications that demonstrate how optimization techniques play a crucial role in the development and refinement of machine learning models. From optimization-based algorithms for training neural networks to optimization-driven strategies for hyperparameter tuning, this edition equips readers with the knowledge and skills needed to leverage optimization effectively in the realm of machine learning.

Key Points:
1. Explore fundamental concepts of optimization theory, including mathematical programming and convex optimization.
2. Gain insights into optimization algorithms and techniques such as gradient descent, constrained optimization, and metaheuristics.
3. Discover the integration of optimization principles with machine learning, with practical applications in training algorithms and optimizing model performance.

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Writer                 ✤              Edwin K. P. Chong (Author),
                                             Stanislaw H. Zak (Author)