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

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

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