Table of Contents

"MLVR - Machine Learning via Rust" is an extensive and forward-thinking textbook that bridges the gap between theoretical machine learning concepts and practical implementation using the Rust programming language. This book is meticulously crafted to provide a deep dive into both the foundational and advanced aspects of machine learning, all through the lens of Rust’s unique strengths in performance, safety, and concurrency. It covers a wide range of topics, from classical machine learning models such as linear regression and neural networks to modern techniques including AutoML and reinforcement learning. With clear explanations, detailed Rust code examples, and real-world applications, "MLVR" equips readers with the knowledge and skills needed to effectively design, implement, and deploy machine learning models. It is designed for students and professionals alike, offering practical guidance and hands-on experience to harness Rust’s capabilities in the rapidly evolving field of machine learning, ultimately preparing readers to tackle complex problems and innovate with cutting-edge technologies.

Main Sections link

• Introduction to Machine Learning via Rust
• Preface
• Foreword

Part I - Foundations link

• Chapter 1 - The Machine Learning Problem
• Chapter 2 - Getting Started with Rust
• Chapter 3 - Mathematics for Machine Learning
• Chapter 4 - Machine Learning Crates in Rust Ecosystem

Part II - Supervised Learning link

• Chapter 5 - Linear Models
• Chapter 6 - Decision Trees and Ensemble Methods
• Chapter 7 - Support Vector Machines
• Chapter 8 - Neural Networks and Back Propagation

Part III - Unsupervised Learning link

• Chapter 9 - Clustering Algorithms
• Chapter 10 - Dimensionality Reduction
• Chapter 11 - Anomaly Detection
• Chapter 12 - Density Estimation and Generative Models

Part IV - Advanced Topics link

• Chapter 13 - Probabilistic Graphical Models
• Chapter 14 - Reinforcement Learning
• Chapter 15 - Kernel Methods
• Chapter 16 - Gradient Boosting Models
• Chapter 17 - AutoML

Part V - Practical Machine Learning with Rust link

• Chapter 18 - Data Processing and Feature Engineering
• Chapter 19 - Model Evaluation and Tuning
• Chapter 20 - Large-Scale Machine Learning
• Chapter 21 - Deploying Machine Learning Models
• Chapter 22 - Machine Learning Operations in Cloud

Part VI - Emerging Trends and Future Directions link

• Chapter 23 - Explainable AI and Interpretability
• Chapter 24 - Federated Learning and Privacy-Preserving ML
• Chapter 25 - Ethics and Fairness in Machine Learning
• Chapter 26 - Quantum Machine Learning
• Chapter 27 - The Future of Machine Learning with Rust

"Machine Learning via Rust (MLVR)" is designed to be the definitive resource for scientists and engineers looking to master machine learning using the Rust programming language, offering a balance of theory, conceptual model, practical application, and insight into future trends.