1. Chapters

• Chapter 1. Introduction: Basic Concepts
• Chapter 2. Probability Distributions
• Chapter 3. Linear Models for Regression
• Chapter 4. Linear Models for Classfication
• Chapter 5. Neural Networks
• Chapter 6. Kernel Methods
• Chapter 7. Sparse Kernel Machines
• Chapter 9. Mixture Models and EM
• Chapter 10. Approximate Inference
• Chapter 11. Sampling Methods
• Cahpter 12. Continous Latent Variable
• Chapter 13. Sequential Data
• Chapter 14. Combining Models

2. Solution

Bishop-Pattern-Recognigion-and-Machine-Learning-2006-Solution