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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

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By TU Delft

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