AIMA

Artificial Intelligence: A Modern Approach

Lectures

Reveal.js slides with TTS narration and captions. Click to open.

• Lecture 0: GitHub Classroom Assignments (Getting Started)
• Lecture 1: Introduction (Artificial Intelligence)
  Accept assignment →
• Lecture 2: Intelligent Agents (Artificial Intelligence)
  Accept assignment →
• Lecture 3: Solving Problems by Searching (Problem-Solving)
  Accept assignment →
• Lecture 4: Search in Complex Environments (Problem-Solving)
  Accept assignment →
• Lecture 5: Adversarial Search and Games (Problem-Solving)
  Accept assignment →
• Lecture 6: Constraint Satisfaction Problems (Problem-Solving)
  Accept assignment →
• Lecture 7: Logical Agents (Knowledge, Reasoning, and Planning)
  Accept assignment →
• Lecture 8: First-Order Logic (Knowledge, Reasoning, and Planning)
  Accept assignment →
• Lecture 9: Inference in First-Order Logic (Knowledge, Reasoning, and Planning)
  Accept assignment →
• Lecture 10: Knowledge Representation (Knowledge, Reasoning, and Planning)
  Accept assignment →
• Lecture 11: Automated Planning (Knowledge, Reasoning, and Planning)
  Accept assignment →
• Lecture 12: Quantifying Uncertainty (Uncertain Knowledge and Reasoning)
  Accept assignment →
• Lecture 13: Probabilistic Reasoning (Uncertain Knowledge and Reasoning)
  Accept assignment →
• Lecture 14: Probabilistic Reasoning over Time (Uncertain Knowledge and Reasoning)
  Accept assignment →
• Lecture 15: Probabilistic Programming (Uncertain Knowledge and Reasoning)
  Accept assignment →
• Lecture 16: Making Simple Decisions (Uncertain Knowledge and Reasoning)
  Accept assignment →
• Lecture 17: Making Complex Decisions (Uncertain Knowledge and Reasoning)
  Accept assignment →
• Lecture 18: Multiagent Decision Making (Uncertain Knowledge and Reasoning)
  Accept assignment →
• Lecture 19: Learning from Examples (Machine Learning)
  Accept assignment →
• Lecture 20: Learning Probabilistic Models (Machine Learning)
  Accept assignment →
• Lecture 21: Deep Learning (Machine Learning)
  Accept assignment →
• Lecture 22: Reinforcement Learning (Machine Learning)
  Accept assignment →
• Lecture 23: Natural Language Processing (Communicating, Perceiving, and Acting)
  Accept assignment →
• Lecture 24: Deep Learning for Natural Language Processing (Communicating, Perceiving, and Acting)
  Accept assignment →
• Lecture 25: Computer Vision (Communicating, Perceiving, and Acting)
  Accept assignment →
• Lecture 26: Robotics (Communicating, Perceiving, and Acting)
  Accept assignment →
• Lecture 27: Philosophy, Ethics, and Safety of AI (Conclusions)
  Accept assignment →
• Lecture 28: The Future of AI (Conclusions)
  Accept assignment →

Assignments

GitHub Classroom assignments — one per lecture. Accept to get your own repo with exercises and AI-assisted tools.

Accept the assignment for each lecture to get your repo. Use Cursor or Codespaces with the curriculum MCP tools.

• Lecture 1: Introduction (Artificial Intelligence)
• Lecture 2: Intelligent Agents (Artificial Intelligence)
• Lecture 3: Solving Problems by Searching (Problem-Solving)
• Lecture 4: Search in Complex Environments (Problem-Solving)
• Lecture 5: Adversarial Search and Games (Problem-Solving)
• Lecture 6: Constraint Satisfaction Problems (Problem-Solving)
• Lecture 7: Logical Agents (Knowledge, Reasoning, and Planning)
• Lecture 8: First-Order Logic (Knowledge, Reasoning, and Planning)
• Lecture 9: Inference in First-Order Logic (Knowledge, Reasoning, and Planning)
• Lecture 10: Knowledge Representation (Knowledge, Reasoning, and Planning)
• Lecture 11: Automated Planning (Knowledge, Reasoning, and Planning)
• Lecture 12: Quantifying Uncertainty (Uncertain Knowledge and Reasoning)
• Lecture 13: Probabilistic Reasoning (Uncertain Knowledge and Reasoning)
• Lecture 14: Probabilistic Reasoning over Time (Uncertain Knowledge and Reasoning)
• Lecture 15: Probabilistic Programming (Uncertain Knowledge and Reasoning)
• Lecture 16: Making Simple Decisions (Uncertain Knowledge and Reasoning)
• Lecture 17: Making Complex Decisions (Uncertain Knowledge and Reasoning)
• Lecture 18: Multiagent Decision Making (Uncertain Knowledge and Reasoning)
• Lecture 19: Learning from Examples (Machine Learning)
• Lecture 20: Learning Probabilistic Models (Machine Learning)
• Lecture 21: Deep Learning (Machine Learning)
• Lecture 22: Reinforcement Learning (Machine Learning)
• Lecture 23: Natural Language Processing (Communicating, Perceiving, and Acting)
• Lecture 24: Deep Learning for Natural Language Processing (Communicating, Perceiving, and Acting)
• Lecture 25: Computer Vision (Communicating, Perceiving, and Acting)
• Lecture 26: Robotics (Communicating, Perceiving, and Acting)
• Lecture 27: Philosophy, Ethics, and Safety of AI (Conclusions)
• Lecture 28: The Future of AI (Conclusions)