Machine Learning: A Bayesian and Optimization Perspective > 인공지능

Chapter 1. Introduction
Chapter 2. Probability and Stochastic Processes
Chapter 3. Learning in Parametric Modeling: Basic Concepts and Directions
Chapter 4: Mean-Square Error Linear Estimation
Chapter 5. Stochastic Gradient Descent: The LMS Algorithm and Its Family
Chapter 6. The Least-Squares Family
Chapter 7. Classification: A Tour of the Classics
Chapter 8. Parameter Learning: A Convex Analytic Path
Chapter 9. Sparsity-Aware Learning: Concepts and Theoretical Foundations
Chapter 10. Sparsity-Aware Learning: Algorithms and Applications
Chapter 11. Learning in Reproducing Kernel Hilbert Spaces
Chapter 12. Bayesian Learning: Inference and the EM Algorithm
Chapter 13. Bayesian Learning: Approximate Inference and Nonparametric Models
Chapter 14. Monte Carlo Methods
Chapter 15. Probabilistic Graphical Models: Part 1
Chapter 16. Probabilistic Graphical Models: Part 2
Chapter 17. Particle Filtering
Chapter 18. Neural Networks and Deep Learning
Chapter 19. Dimensionality Reduction and Latent Variables Modeling