Machine Learning with Julia
An Algorithmic Exploration
Series: Machine Learning: Foundations, Methodologies, and Applications;
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Product details:
- Publisher Springer Nature Singapore
- Date of Publication 17 April 2026
- Number of Volumes 1 pieces, Book
- ISBN 9789819696888
- Binding Hardback
- No. of pages422 pages
- Size 279x210 mm
- Language English
- Illustrations XXIII, 422 p. 126 illus., 110 illus. in color. Illustrations, black & white 696
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Long description:
"
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.
" MoreTable of Contents:
Introduction.- Metrics and Divergences.- Clustering.- Online Clustering.- Dimension Reduction.- Bayesian classification.- Support Vector Machines = Linear Machines + Kernels.- Tree and Forest: Divide-and-Conquer.- Regression and Model Selection.- Ensemble Methods.- Neural networks.- Convolutional neural networks.- Autoencoders.- Generative adversarial networks.- Transfer Learning.- Federated Learning.
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