PRML

Python codes implementing algorithms described in Bishop's book "Pattern Recognition and Machine Learning"

Required Packages

• python 3
• numpy
• scipy
• jupyter (optional: to run jupyter notebooks)
• matplotlib (optional: to plot results in the notebooks)
• sklearn (optional: to fetch data)

Notebooks

The notebooks in this repository can be viewed with nbviewer or other tools, or you can use Amazon SageMaker Studio Lab, a free computing environment on AWS (prior registration with an email address is required. Please refer to this document for usage).

From the table below, you can open the notebooks for each chapter in each of these environments.

|nbviewer|Amazon SageMaker Studio Lab| |:-------|:--------------------------:| |ch1. Introduction|| |ch2. Probability Distributions|| |ch3. Linear Models for Regression|| |ch4. Linear Models for Classification|| |ch5. Neural Networks|| |ch6. Kernel Methods|| |ch7. Sparse Kernel Machines|| |ch8. Graphical Models|| |ch9. Mixture Models and EM|| |ch10. Approximate Inference|| |ch11. Sampling Methods|| |ch12. Continuous Latent Variables|| |ch13. Sequential Data||

If you use the SageMaker Studio Lab, open a terminal and execute the following commands to install the required libraries.

conda env create -f environment.yaml  # might be optional
conda activate prml
python setup.py install