Machine Learning: Foundations, Methodologies, and Applications: An Algorithmic Exploration

Deng, Jeremiah D.

This textbook offers a comprehensive and accessible introduction to machine learning with the Julia programming language. It bridges mathematical theory and real-world practice, guiding readers through both foundational concepts and advanced algorithms. Covering topics from essential principles like Kullback–Leibler divergence and eigen-analysis to cutting-edge techniques such as deep transfer learning and differential privacy, each chapter delivers clear explanations and detailed algorithmic treatments. Sample code accompanies every major topic, enabling hands-on learning and faster implementation.

By leveraging Julia’s powerful machine learning ecosystem—including libraries such as Flux.jl, MLJ.jl, and more—this book empowers readers to build robust, state-of-the-art machine learning models.

Ideal for students, researchers, and professionals alike, this textbook is designed for those seeking a solid theoretical foundation in machine learning, along with deep algorithmic insight and practical problem-solving inspiration.

Details

Published by: Springer

Publication Date: 2026-04-17

Format: Hardcover

ISBN-13: 9789819696888

DOI: 10.1007/978-981-96-9689-5

Dimensions: 279cm x210cm

Pages: 422