Solutions for Pattern Recognition and Machine Learning - Christopher M. Bishop
This repo contains (or at least will eventually contain) solutions to all the exercises in Pattern Recognition and Machine Learning - Christopher M. Bishop, along with useful code snippets to illustrate certain concepts.
Note: View the solutions at https://priyathamkat.com/bishop-prml/ as GitHub doesn't render LaTeX in
.ipynb notebooks properly.
- Introduction (15/41)
- Probability Distributions (0/61)
- Linear Models for Regression (0/24)
- Linear Models for Classification (0/26)
- Neural Networks (0/41)
- Kernel Methods (0/27)
- Sparse Kernel Machines (0/19)
- Graphical Models (0/29)
- Mixture Models and EM (0/27)
- Approximate Inference (0/39)
- Sampling Methods (0/17)
- Continuous Latent Variables (0/29)
- Sequential Data (0/34)
- Combining Models (0/17)
Please raise an issue if you notice any inaccuracies.