</>SkillByAI
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Artificial Intelligence

The foundations of AI in one course: agents, search, A*, game playing, constraints, logic, probability, decisions, learning, reinforcement learning, language models and ethics, with every algorithm implemented and run in plain Python.

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What you'll learn

  • Explain what AI is, how it developed, and how intelligent agents perceive and act in environments.
  • Solve problems with search: breadth-first, depth-first, A* with heuristics, and local search.
  • Use game-tree search, constraint satisfaction and rule-based reasoning on small problems.
  • Reason under uncertainty with Bayes' rule, naive Bayes and expected utility.
  • Describe how machines learn, from the perceptron to reinforcement learning and language models.
  • Discuss the ethical, safety and societal questions raised by modern AI systems.

Syllabus

What Artificial Intelligence Is

  1. Definitions and a Short History
  2. Intelligent Agents and Environments
  3. Kinds of AI

Solving Problems by Search

  1. Formulating a Search Problem
  2. Breadth-First and Depth-First Search
  3. A* Search and Heuristics
  4. Local Search: Hill Climbing and Simulated Annealing

Games and Constraints

  1. Minimax: Playing Against an Opponent
  2. Alpha-Beta Pruning
  3. Constraint Satisfaction Problems

Knowledge and Reasoning

  1. Logic and Rule-Based Reasoning
  2. Knowledge Graphs
  3. The Limits of Symbolic AI

Reasoning Under Uncertainty

  1. Probability and Bayes' Rule
  2. Naive Bayes Classification
  3. Decisions and Expected Utility

Learning Agents

  1. Learning From Examples: The Perceptron
  2. Markov Decision Processes and Value Iteration
  3. Reinforcement Learning: Q-Learning

Modern AI: Language Models and Agents

  1. Language Models: Predicting the Next Word
  2. Deep Learning at Scale
  3. AI Agents With Tools

AI, Ethics and Society

  1. Bias and Fairness
  2. Safety, Privacy and Regulation
  3. A Responsible AI Checklist