Phase 3: Advanced Math for AI · ~90 minutes · Python

Graph Theory for Machine Learning

Graphs are the data structure of relationships. If your data has connections, you need graph theory.

Hiring signal: Understanding of graph theory for machine learning internals

What you will learn

Build a graph class with adjacency matrix/list representations and implement BFS and DFS traversals
Compute the graph Laplacian and use its eigenvalues to detect connected components and cluster nodes
Implement one round of GNN-style message passing as a normalized adjacency matrix multiplication
Apply spectral clustering to partition a graph using the Fiedler vector

Introduction

Type: Build Language: Python Prerequisites: Phase 1, Lessons 01-03 (linear algebra, matrices) Time: ~90 minutes

Learning Objectives

Build a graph class with adjacency matrix/list representations and implement BFS and DFS traversals
Compute the graph Laplacian and use its eigenvalues to detect connected components and cluster nodes
Implement one round of GNN-style message passing as a normalized adjacency matrix multiplication
Apply spectral clustering to partition a graph using the Fiedler vector

Unlock the full lesson

You've read the first 2 sections. The rest of this lesson covers The Problem, The Concept, Build It, Use It, Ship It, Connections, Exercises, Key Terms, Further Reading — plus a hands-on lab, quiz, and project artifact.

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