An Introduction to Modern
Statistical Learning
Preface
1
Introduction
I
Representation and Inference
II
Learning
4
A Mathematical Framework for Learning
4.1
Learning as optimization
4.2
Minimizing relative entropy and maximizing likelihood
5
Learning Discriminative Models
6
Learning Generative Models with Latent Variables
7
Learning Invertible Generative Models
8
Learning Non-Invertible Generative Models
9
Learning with Reparameterizations
10
Learning Energy-Based Models
Appendix
A
Bonus Material
B
Mathematical Appendix
C
A Review of Probabilistic Graphical Models
Bibliography
A Mathematical Framework for Learning
Chapter 4
A Mathematical Framework for Learning
4.1
Learning as optimization
4.2
Minimizing relative entropy and maximizing likelihood