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.
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
Solving Problems by Search
- Formulating a Search Problem
- Breadth-First and Depth-First Search
- A* Search and Heuristics
- Local Search: Hill Climbing and Simulated Annealing
Games and Constraints
Knowledge and Reasoning
Reasoning Under Uncertainty
Learning Agents
- Learning From Examples: The Perceptron
- Markov Decision Processes and Value Iteration
- Reinforcement Learning: Q-Learning