Lesson 303
AI: Knowledge & Reasoning
Logic · Uncertainty · Planning
1:00The classical, reasoning side of AI — logic, Bayesian probability, constraint satisfaction, and planning — and how it fuses with machine learning in modern neuro-symbolic systems.
By the end, you can
- Explain that AI encompasses search, logic, uncertainty, CSP, planning, and learning — not just ML.
- Distinguish propositional logic from first-order logic and give an example of each.
- Trace a modus ponens inference through a small knowledge base.
- Apply Bayes' rule to compute a posterior probability and identify the base-rate fallacy.
- Define a CSP using variables, domains, and constraints, and trace one step of backtracking.
- Describe a STRIPS action using preconditions and effects, and construct a short plan.
- Compare symbolic and sub-symbolic AI, and explain what neuro-symbolic AI combines.
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