Abstract
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.
The chapters in this book are free to read via Springer Nature SharedIt Initiative using the chapter links below:
Preamble:
Introduction pp. 3-16 - https://rdcu.be/feU8I
Metrics and Divergences pp. 17-33 - https://rdcu.be/feU8K
Unsupervised Learning:
Clustering pp. 37-68 - https://rdcu.be/feVaa
Online Clustering pp. 69-92 - https://rdcu.be/feVak
Dimension Reduction pp. 93-134 - https://rdcu.be/feVau
Supervised Learning:
Discrimant Function Models pp. 137-158 - https://rdcu.be/feVa0
Local Models pp. 159-180 - https://rdcu.be/feVa1
Performance Evaluation pp. 181-192 - https://rdcu.be/feVbd
Regression pp. 193-215 - https://rdcu.be/feVbe
Feature Ranking and Selection pp. 217-237 - https://rdcu.be/feVbf
Model Selection pp. 239-251 - https://rdcu.be/feVbh
Ensembles pp. 253-278 - https://rdcu.be/feVbn
Neural Networks and Deep Learning:
Multilayer Neural Networks pp. 283-309 - https://rdcu.be/feVcn
Autoencoder and Generative Models pp. 311-332 - https://rdcu.be/feVcv
Transfer Learning pp. 333-353 - https://rdcu.be/feVcw
Federated Learning pp. 355-368 - https://rdcu.be/feVdK