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Introduction
Pre-requisite Knowledge
Probability Theory
Graph Models
Bayes’ Theorem
The Gaussian Distribution
Preliminaries
Your very first models
Frequentist Regression
Introduction and k-Nearest Neighbors
Decision Theory
Linear Basis Function Models
Regularization and Stochastic Gradient Descent
Feedforward Neural Networks
Bayesian Regression
Linear Basis Function Models
Exercise: Bayesian models versus bootstrapping
Solution: Bayesian models versus bootstrapping
Active Learning
Learning and Model Selection
Exercise: Combining bayesian linear models and neural networks
Solution: Combining bayesian linear models and neural networks
Gaussian Processes
From Weights to Functions
Gaussian Processes
GPs for Regression
Empirical Bayes
Exercise: Gaussian processes
Solution: Gaussian processes
Index
