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Overview of AI/ML

Artificial Intelligence (AI) and Machine Learning (ML) are transforming technology, enabling systems to perform tasks that mimic human intelligence. This section introduces the core concepts of AI and ML, setting the foundation for your journey into building intelligent systems with Rust.

What is AI?

AI is the field of computer science focused on creating systems capable of tasks like reasoning, problem-solving, and perception. Examples include voice assistants, autonomous vehicles, and game-playing agents. AI subfields include machine learning, natural language processing (NLP), computer vision, and reinforcement learning.

What is ML?

Machine Learning, a subset of AI, allows systems to learn from data without explicit programming. ML algorithms identify patterns to make predictions or decisions, such as predicting house prices or classifying images.

Types of ML

• Supervised Learning: Models learn from labeled data (e.g., regression, classification). Algorithms include linear regression and support vector machines.
• Unsupervised Learning: Models find patterns in unlabeled data (e.g., clustering, PCA).
• Reinforcement Learning: Agents learn by interacting with an environment to maximize rewards (e.g., training a robot to navigate).

Statistical Learning and Mathematics

ML relies on statistical learning, using mathematical principles to model data. Key areas include:

• Linear Algebra: Vectors and matrices for data representation.
• Calculus: Gradients for model optimization.
• Probability and Statistics: Uncertainty quantification and evaluation.

These concepts are explored in depth in the Mathematical Foundations module.

Why Learn AI/ML with Rust?

Rust’s performance and safety make it ideal for ML, supported by libraries like linfa and tch-rs. This tutorial combines theory, math, and hands-on Rust labs to master AI/ML.

Next Steps

Explore Why Rust for AI/ML to understand Rust’s advantages, or check Tools for the software used in this tutorial.

Further Reading

• An Introduction to Statistical Learning by James et al. (Chapter 1)
• Andrew Ng’s Machine Learning Specialization (Course 1, Week 1)
• The Hundred-Page Machine Learning Book by Burkov (Chapter 1)