Browse the full book, chapter by chapter.
Use this guide to move through the book by topic area, jump into a chapter that matches your interests, and follow the concepts that connect across search, optimization, learning, and generative AI.
Foundations
Start with the mental models behind AI, then move into search, planning, and game-playing.
What Is Artificial Intelligence?
Explore what intelligence means, why data powers AI, and how algorithms act like recipes for problem-solving.
Chapter 2Search fundamentals
Compare breadth-first and depth-first search in a maze and see how planning becomes systematic exploration.
Chapter 3Intelligent search
Go beyond blind search with heuristics, minimax, and alpha-beta pruning in adversarial settings.
Optimization
Explore evolutionary and swarm-based methods for navigating large solution spaces.
Evolutionary algorithms
Learn how selection, crossover, and mutation improve solutions over generations in the knapsack problem.
Chapter 5Advanced evolutionary approaches
See how encoding, selection pressure, and elitism shape what evolutionary algorithms can discover.
Chapter 6Swarm intelligence: Ants
Use ant colony optimization to find better routes and understand emergent behavior through pheromone trails.
Chapter 7Particle Swarm Optimization
Balance inertia, personal best, and swarm best to optimize a drone-inspired search problem.
Learning Systems
Build intuition for machine learning, neural networks, and reinforcement learning.
Machine learning
Fit a linear regression model and learn how features, loss, and learning rate shape predictions.
Chapter 9Artificial neural networks
Connect perceptrons, hidden layers, and backpropagation to a small network that learns from driving data.
Chapter 10Reinforcement learning
Train a Q-learning agent in a grid world and watch rewards shape better actions over time.
Generative AI
Bridge from language modeling into modern image generation systems.
Grokking AI Algorithms
How AI solves complex problems
A practical, visual guide to the algorithms that power search, machine learning, neural networks, LLMs, and generative AI.
