Artificial Intelligence
Problem-solving
Problems & Environments Explorer
Animate problem formulations, classic AI tasks, and environment properties.
Open explorer → 03Solving Problems by Searching
Problem-solving agents, search algorithms, uninformed and heuristic strategies.
Explore chapter → 04Search in Complex Environments
Local optimization, nondeterminism, partial observability, and online search.
Explore chapter → 05Adversarial Search and Games
Game theory, minimax, alpha–beta, Monte Carlo trees, and stochastic games.
Explore chapter → 06Constraint Satisfaction Problems
Constraint propagation, backtracking, local search, and problem structure.
Explore chapter →Knowledge, reasoning, and planning
Logical Agents
Knowledge-based agents, the Wumpus world, propositional logic, and model checking.
Explore chapter → 08First-Order Logic
Representation, syntax, semantics, use, and knowledge engineering.
Explore chapter → 09Inference in First-Order Logic
Unification, forward and backward chaining, and resolution.
Explore chapter → 10Knowledge Representation
Ontologies, categories, events, mental objects, and default reasoning.
Explore chapter → 11Automated Planning
Classical planning, heuristics, hierarchy, uncertainty, time, and resources.
Explore chapter →Uncertain knowledge and reasoning
Quantifying Uncertainty
Probability, independence, Bayes’ rule, naive Bayes, and uncertainty in worlds.
Explore chapter → 13Probabilistic Reasoning
Bayesian networks, exact and approximate inference, and causal networks.
Explore chapter → 14Probabilistic Reasoning over Time
Temporal models, hidden Markov models, Kalman filters, and dynamic networks.
Explore chapter → 15Probabilistic Programming
Relational and open-universe models, complex worlds, and programs as models.
Explore chapter → 16Making Simple Decisions
Beliefs and desires, utility, decision networks, and value of information.
Explore chapter → 17Making Complex Decisions
Sequential decisions, MDPs, bandits, POMDPs, and solution methods.
Explore chapter → 18Multiagent Decision Making
Multiagent settings, non-cooperative and cooperative games, collective choice.
Explore chapter →Machine Learning
Learning from Examples
Supervised learning, trees, optimization, regression, ensembles, and practice.
Explore chapter → 20Learning Probabilistic Models
Statistical learning, complete data, and expectation-maximization.
Explore chapter → 21Deep Learning
Neural networks, computation graphs, convolution, learning, and transfer.
Explore chapter → 22Reinforcement Learning
Rewards, passive and active learning, generalization, and policy search.
Explore chapter →Communicating, perceiving, and acting
Natural Language Processing
Language models, grammar, parsing, and practical language tasks.
Explore chapter → 24Deep Learning for NLP
Embeddings, sequence models, transformers, pretraining, and transfer learning.
Explore chapter → 25Computer Vision
Image formation, features, classification, object detection, and 3D vision.
Explore chapter → 26Robotics
Hardware, perception, planning, control, uncertainty, and human-robot interaction.
Explore chapter →