Learning Theory and VC Dimension

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

Contents