Learning Theory
Learning Theory
Welcome to the Learning Theory section! Here, you’ll find blog posts exploring the fundamental principles of how machines learn. Topics you can expect include:
- PAC Learning & Generalization – Understanding the Probably Approximately Correct framework
- Bias-Variance Tradeoff – Insights into model capacity and performance
- Sample Complexity – How much data is enough for reliable learning
- Theoretical Guarantees – Bounds, convergence, and proof ideas
This section is perfect for anyone who wants to understand the theory behind ML algorithms before diving into coding or applied experiments.
Blog Posts in Learning Theory
PAC Learning – A Gentle Introduction
Sample Post for Test
Published on September 15, 2025