Learning path 01
Foundations of AI
Learn how intelligent systems represent rules, explore possible actions, satisfy constraints, and turn symbolic goals into plans.
What you will learn
- Translate a problem into states, actions, rules, or constraints.
- Compare uninformed and heuristic search strategies.
- Understand when symbolic methods remain the right tool.
Prerequisites
Basic programming familiarity helps, but this path begins with first principles. Work through rules and search before moving into constraints and planning.
Published tutorials
Cosine Similarity: The Math Behind Semantic Search
Semantic search and RAG rank results by cosine similarity — but what does that actually measure? Build it from the dot product, see the…
Classical Planning (STRIPS): From Symbols to Sequencing
Blocks World → Warehouse Robot Task Scheduling Why Classical Planning? When you ask a robot “put all the blue boxes on shelf A, then…
Constraint Satisfaction (Sudoku Solver): From Logic to Lightning-Fast Solvers
Sudoku Game → Automated Puzzle-Solving Apps Why CSP for Sudoku? Constraint Satisfaction Problems (CSPs) let you describe what must be true (constraints) and let…
Constraint Satisfaction Problems (CSP): Solve Complex Choices with Rules
Map Coloring → Timetable Scheduling Why Constraint Satisfaction Problems? Many real-world tasks are “fill in the blanks but obey rules.” Think exam timetables (no…
Heuristic Search (A*): Shortest Paths with Smarts
Grid Pathfinding → GPS Navigation Why Heuristic Search? When you ask a maps app for the fastest route, it can’t try every possible path—there…
Search Algorithms (BFS & DFS): From Mazes to Motion Plans
8-Puzzle Solver → Route Finding in Robotics Why Search Algorithms? Breadth-First Search (BFS) and Depth-First Search (DFS) are the bedrock of problem solving in…
Rule-based Systems: The Foundation of AI
Tiny Expert System for Lenses → Medical Diagnosis Expert Systems Introduction to the Applied AI Tutorials Series Welcome to the first article of the…