An Introduction to Modern
Statistical Learning
Preface
1
Introduction
I
Representation and Inference
II
Learning
4
A Mathematical Framework for Learning
5
Learning Discriminative Models
6
Learning Generative Models with Latent Variables
7
Learning Invertible Generative Models
8
Learning Non-Invertible Generative Models
8.1
Gaussian recognition distributions and sparse coding
8.2
Variational Autoencoders
8.3
Diffusion Models
8.4
Variational inference
9
Learning with Reparameterizations
10
Learning Energy-Based Models
Appendix
A
Bonus Material
B
Mathematical Appendix
C
A Review of Probabilistic Graphical Models
Bibliography
Variational inference
8.4
Variational inference