Introduction to Probabilistic Machine Learning
Welcome
Preface
Preface
Python
Notation
Fundamentals
1. Fundamentals of Probability Theory
1.1. Probability Spaces
1.2. Random Variables
1.3. Independence
1.4. Important Probability Distributions
1.5. Essential Theorems
2. Bayesian vs. Frequentists View
3. Bayesian Inference, MAP & MLE
3.1. Coin Toss
3.2. Bayesian Inference
3.3. MAP and MLE
3.4. Linear Regression
4. Optimization Methods
5. Machine Learning Workflow
Probabilistic Machine Learning
6. Motivation of Probabilistic Models
7. Gaussian Processes for Machine Learning
7.1. The Kernel Trick: Implicit embeddings from inner products
7.2. Gaussian Processes
7.3. Gaussian Process Regression
7.4. Kernel Functions
7.5. Impact of Hyperparameters
7.6. Selection of Hyperparameters
7.7. Extension to Multiple Outputs
7.8. Gaussian Process Classification
7.9. Examples
7.10. Advanced Methods
7.10.1. Scalable Gaussian Processes
7.10.2. Non-stationary Gaussian Processes
7.10.3. Gaussian Processes on latent representations
8. Overview of Further Probabilistic Models
Applications
9. Bayesian Optimization
10. Efficient Reinforcement Learning
repository
Index
