Table of contents

Artificial Intelligence from First Principles

A rigorous, practical path from core ideas to building and evaluating modern AI systems

Read each section in order. Every title can be opened as a TheoryTrace document.

• Cover
• Copyright
• How to read this book
• Introduction
• Chapter 1: What Artificial Intelligence Is
• Chapter 2: The Computational View of Intelligence
• Chapter 3: Essential Mathematics for AI
• Chapter 4: Data, Features, and Measurement
• Chapter 5: Learning as Generalization
• Chapter 6: Supervised Learning
• Chapter 7: Evaluating AI Models
• Chapter 8: Unsupervised Learning and Representation
• Chapter 9: Optimization and Training
• Chapter 10: Neural Networks
• Chapter 11: Deep Learning for Vision, Language, and Signals
• Chapter 12: Natural Language Processing
• Chapter 13: Large Language Models
• Chapter 14: Generative AI
• Chapter 15: Retrieval, Tools, and AI Applications
• Chapter 16: Reasoning, Planning, and Agents
• Chapter 17: Reinforcement Learning
• Chapter 18: Building an AI System End to End
• Chapter 19: AI Engineering and MLOps
• Chapter 20: Safety, Security, and Robustness
• Chapter 21: Ethics, Law, and Social Impact
• Chapter 22: The Frontier and Your Learning Roadmap
• Conclusion