Study notes from Bishop's book
| Chapter | Read | Exercises |
|---|---|---|
| 1. Introduction | ||
| 2. Probability Distributions | ||
| 3. Linear Models for Regression | ||
| 4. Linear Models for Classification | ||
| 5. Neural Networks | ||
| 6. Kernel Methods | ||
| 7. Sparse Kernel Machines | ||
| 8. Graphical Models | ||
| 9. Mixture Models and EM | ||
| 10. Approximate Inference | ||
| 11. Sampling Methods | ||
| 12. Continuous Latent Variables | ||
| 13. Sequential Data | ||
| 14. Combining Models |