CS188 Intro to AI

Table of contents

1. Ⅰ Artificial Intelligence
2. Ⅱ Problem-solving
3. Ⅲ Knowledge, reasoning,and planning
4. Ⅳ Uncertain knowledge and reasoning
5. Ⅴ Machine Learning
6. Ⅵ Communicating,perceiving, and acting
7. VIl Conclusions
8. A Mathematical Background
9. B Notes on Languages and Algorithms

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Ⅰ Artificial Intelligence

• 1 Introduction
  • 1.1 What is Al?
  • 1.2 The Foundations of Artificial Intelligence
  • 1.3 The History of Artificial Intelligence
  • 1.4 The State of the Art
  • 1.5 Risks and Benefits of Al
  • Summary
  • Bibliographical and Historical Notes
• 2 Intelligent Agents
  • 2.1 Agents and Environments
  • 2.2 Good Behavior: The Concept of Rationality
  • 2.3 The Nature of Environments
  • 2.4 The Structure of Agents
  • Summary
  • Bibliographical and Historical Notes

Ⅱ Problem-solving

• 3 Solving Problems by Searching
  • 3.1 Problem-Solving Agents
  • 3.2 Example Problems
  • 3.3 Search Algorithms
  • 3.4 Uninformed Search Strategies
  • 3.5 Informed (Heuristic) Search Strategies
  • 3.6 Heuristic Functions
  • Summary
  • Bibliographical and Historical Notes
• 4 Search in Complex Environments
  • 4.1 Local Search and Optimization Problems
  • 4.2 Local Search in Continuous Spaces
  • 4.3 Search with Nondeterministic Actions
  • 4.4 Search in Partially Observable Environments
  • 4.5 Online Search Agents and Unknown Environments
  • Summary
  • Bibliographical and Historical Notes
• 5 Adversarial Search and Games
  • 5.1 Game Theory
  • 5.2 Optimal Decisions in Games
  • 5.3 Heuristic Alpha-Beta Tree Search
  • 5.4 Monte Carlo Tree Search
  • 5.5 Stochastic Games
  • 5.6 Partially Observable Games
  • 5.7 Limitations of Game Search Algorithms
  • Summary
  • Bibliographical and Historical Notes
• 6 Constraint Satisfaction Problems
  • 6.1 Defining Constraint Satisfaction Problems
  • 6.2 Constraint Propagation: Inference in CSPs
  • 6.3 Backtracking Search for CSPs
  • 6.4 Local Search for CSPs
  • 6.5 The Structure ofProblems
  • Summary
  • Bibliographical and Historical Notes

Ⅲ Knowledge, reasoning,and planning

• 7 Logical Agents
  • 7.1 Knowledge-Based Agents
  • 7.2 The Wumpus World
  • 7.3 Logic
  • 7.4 Propositional Logic: A Very Simple Logic
  • 7.5 Propositional Theorem Proving
  • 7.6 Effective Propositional Model Checking
  • 7.7 Agents Based on Propositional Logic
  • Summary
  • Bibliographical and Historical Notes
• 8 First-Order Logic
  • 8.1 Representation Revisited
  • 8.2 Syntax and Semantics of First-Order Logic
  • 8.3 Using First-Order Logic
  • 8.4 Knowledge Engineering in First-Order Logic
  • Summary
  • Bibliographical and Historical Notes
• 9 Inference in First-Order Logic
  • 9.1 Propositional vs. First-Order Inference
  • 9.2 Unification and First-Order Inference
  • 9.3 Forward Chaining
  • 9.4 Backward Chaining
  • 9.5 Resolution
  • Summary
  • Bibliographical and Historical Notes
• 10 Knowledge Representation
  • 10.1 Ontological Engineering
  • 10.2 Categories and Objects
  • 10.3 Events
  • 10.4 Mental Objects and Modal Logic
  • 10.5 Reasoning Systems for Categories
  • 10.6 Reasoning with Default Information
  • Summary
  • Bibliographical and Historical Notes
• 11 Automated Planning
  • 11.1 Definition of Classical Planning
  • 11.2 Algorithms for Classical Planning
  • 11.3 Heuristics for Planning
  • 11.4 Hierarchical Planning
  • 11.5 Planning and Acting in Nondeterministic Domains
  • 11.6 Time, Schedules, and Resources
  • 11.7 Analysis of Planning Approaches
  • Summary
  • Bibliographical and Historical Notes

Ⅳ Uncertain knowledge and reasoning

• 12 Quantifying Uncertainty
  • 12.1 Acting under Uncertainty
  • 12.2 Basic Probability Notation
  • 12.3 Inference Using Full Joint Distributions
  • 12.4 Independence
  • 12.5 Bayes’ Rule and Its Use
  • 12.6 Naive Bayes Models
  • 12.7 The Wumpus World Revisited
  • Summary
  • Bibliographical and Historical Notes
• 13 Probabilistic Reasoning
  • 13.1 Representing Knowledge in an Uncertain Domain
  • 13,2 The Semantics of Bayesian Networks
  • 13.3 Exact Inference in Bayesian Networks
  • 13.4 Approximate Inference for Bayesian Networks
  • 13.5 Causal Networks
  • Summary
  • Bibliographical and Historical Notes
• 14 Probabilistic Reasoning over Time
  • 14.1 Time and Uncertainty
  • 14.2 Inference in Temporal Models
  • 14.3 Hidden Markov Models
  • 14.4 Kalman Filters
  • 14.5 Dynamic Bayesian Networks
  • Summary
  • Bibliographical and Historical Notes
• 15 Probabilistic Programming
  • 15.1 Relational Probability Models
  • 15.2 Open-Universe Probability Models
  • 15.3 Keeping Track of a Complex World
  • 15.4 Programs as Probability Models
  • Summary
  • Bibliographical and Historical Notes
• 16 Making Simple Decisions
  • 16.1 Combining Beliefs and Desires under Uncertainty
  • 16.2 The Basis of Utility Theory
  • 16.3 Utility Functions
  • 16.4 Multiattribute Utlity Functions
  • 16.5 Decision Networks
  • 16.6 The Value of Information
  • 16.7 Unknown Preferences
  • Summary
  • Bibliographical and Historical Notes
• 17 Making Complex Decisions
  • 17.1 Sequential Decision Problems
  • 17.2 Algorithms for MDPs
  • 17.3 Bandit Problems
  • 17.4 Partially Observable MDPs
  • 17.5 Algorithms for Solving POMDPs
  • Summary
  • Bibliographical and Historical Notes
• 18 Multiagent Decision Making
  • 18.1 Properties of Multiagent Environments
  • 18.2 Non-Cooperative Game Theory
  • 18.3 Cooperative Game Theory
  • 18.4 Making Collective Decisions
  • Summary
  • Bibliographical and Historical Notes

Ⅴ Machine Learning

• 19 Learning from Examples
  • 19.1 Forms of Learning
  • 19.2 Supervised Learning
  • 19.3 Learning Decision Trees
  • 19.4 Model Selection and Optimization
  • 19.5 The Theory of Learning
  • 19.6 Linear Regression and Classification
  • 19.7 Nonparametric Models
  • 19.8 Ensemble Learning
  • 19.9 Developing Machine Learning Systems
  • Summary
  • Bibliographical and Historical Notes
• 20 Learning Probabilistic Models
  • 20.1 Statistical Learning
  • 20.2 Learning with Complete Data
  • 20.3 Learning with Hidden Variables: The EM Algorithm
  • Summary
  • Bibliographical and Historical Notes
• 21 Deep Learning
  • 21.1 Simple Feedforward Networks
  • 21.2 Computation Graphs for Deep Learning
  • 21.3 Convolutional Networks
  • 21.4 Learning Algorithms
  • 21.5 Generalization
  • 21.6 Recurrent Neural Networks
  • 21.7 Unsupervised Learning and Transfer Learning
  • 21.8 Applications
  • Summary
  • Bibliographical and Historical Notes
• 22 Reinforcement Learning
  • 22.1 Learning from Rewards
  • 22.2 Passive Reinforcement Learning
  • 22.3 Active Reinforcement Learning
  • 22.4 Generalization in Reinforcement Learning
  • 22.5 Policy Search
  • 22.6 Apprenticeship and Inverse Reinforcement Learning
  • 22.7 Applications of Reinforcement Learning
  • Summary
  • Bibliographical and Historical Notes

Ⅵ Communicating,perceiving, and acting

• 23 Natural Language Processing
  • 23.1 Language Models
  • 23.2 Grammar
  • 23.3 Parsing
  • 23.4 Augmented Grammars
  • 23.5 Complications of Real Natural Language
  • 23.6 Natural Language Tasks
  • Summary
  • Bibliographical and Historical Notes
• 24 Deep Learning for Natural Language Processing
  • 24.1 Word Embeddings
  • 24.2 Recurrent Neural Networks for NLP
  • 24.3 Sequence-to-Sequence Models
  • 24.4 The Transformer Architecture
  • 24.5 Pretraining and Transfer Learning
  • 24.6 State of the art
  • Summary
  • Bibliographical and Historical Notes
• 25 Computer Vision
  • 25.1 Introduction
  • 25.2 lmage Formation
  • 25.3 Simple lmage Features
  • 25.4 Classifying lmages
  • 25.5 Detecting Objects
  • 25.6 The 3D World
  • 25.7 Using Computer Vision
  • Summary
  • Bibliographical and Historical Notes
• 26 Robotics
  • 26.1 Robots
  • 26.2 Robot Hardware
  • 26.3 What kind of problem is robotics solving?
  • 26.4 Robotic Perception
  • 26.5 Planning and Control
  • 26.6 Planning Uncertain Movements
  • 26.7 Reinforcement Learning in Robotics
  • 26.8 Humans and Robots
  • 26.9 Alternative Robotic Frameworks
  • 26.10 Application Domains
  • Summary
  • Bibliographical and Historical Notes

VIl Conclusions

• 27 Philosophy, Ethics, and Safety of Al
  • 27.1 The Limits of Al
  • 27.2 Can Machines Really Think?
  • 27.3 The Ethics of Al
  • Summary
  • Bibliographical and Historical Notes
• 28 The Future of Al
  • 28.1 Al Components
  • 28,2 Al Architectures

A Mathematical Background

• A.1 Complexity Analysis and O() Notation
• A.2 Vectors, Matrices, and Linear Algebra
• A.3 Probability Distributions
• Bibliographical and Historical Notes

B Notes on Languages and Algorithms

• B.1 Defning Languages with Backus-Naur Form(BNF)
• B.2 Describing Algorithms with Pseudocode

• B.3 Online Supplemental Material

• Bibliography