Classical AI
Timeless classical AI methods: state-space search, heuristics, A*, minimax, constraint satisfaction problems, and Bayesian networks.
Course Syllabus & Units
Search
2 lessonsA* and heuristics
**A\*** is a best-first search that uses path cost so far plus a **heuristic** guess of remaining cost—classic informed search.
Search and planning
**Search and planning** means exploring possible actions or solutions in a structured way—like choosing a route—rather than guessing one answer in a single breath.
Uncertainty
3 lessonsBayesian networks intro
A **Bayesian network** is a graph of variables with arrows meaning direct probabilistic influence—plus tables that quantify those links.
Hidden Markov Models intro
A **Hidden Markov Model (HMM)** assumes a hidden state that evolves over time, while you observe noisy signals. Classic tool for speech and sequence labeling before deep end-to-end models dominated many tasks.
Uncertainty and Bayesian thinking
**Uncertainty** is not knowing for sure. **Bayesian thinking** is a disciplined way to update beliefs when new evidence arrives—starting from a prior guess, then revising.