# Learning Theory and VC Dimension

*Student seminar — Session 23*

## Focus

Computational learning theory at a survey level: PAC learning, hypothesis space capacity, VC dimension, and generalization bounds.

## Reading (examples)

- Zaki & Meira, learning theory chapter
- Duda et al., relevant statistical learning sections
- Shalev-Shwartz & Ben-David, *Understanding Machine Learning* (selected sections)

## Student tasks

- Define PAC learnability informally and formally
- Explain VC dimension and its role in model capacity
- Relate theory to bias–variance and empirical model selection
- Q&A and short discussion