Tutorial Roadmap

This section outlines the AI/ML in Rust tutorial, guiding you through 9 modules to master artificial intelligence and machine learning with Rust’s performance and safety.

Modules Overview

1. Introduction: Understand AI/ML basics, Rust’s advantages, and the tools used.
2. Getting Started: Set up Rust, learn basics, and build your first ML model.
3. Mathematical Foundations: Master linear algebra, calculus, probability, and statistics for ML.
4. Core Machine Learning: Explore regression, classification, clustering, and evaluation techniques.
5. Deep Learning: Dive into neural networks, CNNs, RNNs, and optimization.
6. Practical ML Skills: Preprocess data, tune models, and deploy with Rust.
7. Advanced Topics: Study NLP, computer vision, ethics, and reinforcement learning.
8. Projects: Apply skills in real-world projects like house price prediction and image classification.
9. Resources: Access Rust ML libraries, recommended reading, and communities.

Learning Path

Start with the Introduction module to grasp AI/ML concepts and Rust’s role. Progress linearly through Getting Started and Mathematical Foundations to build a strong foundation. Core Machine Learning and Deep Learning cover essential algorithms, while Practical ML Skills and Advanced Topics add real-world and cutting-edge topics. Complete Projects to apply your skills, and explore Resources for further learning.

Next Steps

Begin with Setup to install Rust and start coding, or review Tools for the tutorial’s software.

Further Reading

• An Introduction to Statistical Learning by James et al. (Chapter 1)
• Andrew Ng’s Machine Learning Specialization (Course 1, Week 1)
• Artificial Intelligence: A Modern Approach by Russell and Norvig (Chapter 1)