Semester 1 · Core
Classical AI: Search, Logic & Reasoning
https://epochschool.com/subjects/classical-ai
The ideas AI was built on and that modern agents still rely on: formulating problems as search, uninformed, informed and adversarial search, propositional and first-order logic, Bayesian networks with exact and approximate inference, and the basics of planning and sequential decisions.
- Prerequisites
- Recommended: Helpful background. You can start without it if you are willing to fill the gaps.
What you’ll be able to do
- LO1Formulate a problem as a search over states, actions and goals.
- LO2Implement and compare uninformed, informed and adversarial search algorithms and reason about their optimality and cost.
- LO3Represent knowledge in propositional and first-order logic and decide entailment by model checking and inference.
- LO4Build Bayesian networks and perform exact inference by variable elimination and approximate inference by sampling.
- LO5Solve simple planning and sequential decision problems, including value iteration on Markov decision processes.
Chapters
- 01Agents and problems as search
- 02Uninformed search
- 03Informed search
- 04Adversarial search
- 05Propositional logic
- 06First-order (predicate) logic
- 07Reasoning under uncertainty with Bayesian networks
- 08Planning and sequential decisions
Graded labs
- L1
Route finding on a rail map
After chapter 3 · about 5 hours · Illustrative rail network · Authored for this course, CC0 · Runs on a CPU
- L2
A game-playing agent
After chapter 4 · about 5 hours · Tic-tac-toe rules and test positions · Authored for this course, CC0 · Runs on a CPU
- L3
A SAT solver from scratch
After chapter 5 · about 5 hours · Random and planted 3-CNF formulas · Authored for this course, CC0 · Runs on a CPU
- L4
Inference in a Bayesian network
After chapter 7 · about 5 hours · Monsoon-morning station network · Authored for this course, CC0 · Runs on a CPU
- L5
Planning in a grid world
After chapter 8 · about 5 hours · Campus delivery grid world · Authored for this course, CC0 · Runs on a CPU
Before you start
Recommended
Helpful background. You can start without it if you are willing to fill the gaps.
Where it leads
Responsible AI, Ethics & Governance
Probabilistic ML & Bayesian Methods
Reinforcement Learning Foundations
Capstone Project
How it’s taught
You read short sections written for the page. Each ends with a check, and in-browser exercises let you practise. Graded labs run on your own cloud lab machine, CPU-friendly.
No videos. No live classes. No AI tutor.