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PRML

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

Required Packages

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.

nbviewerAmazon SageMaker Studio Lab
ch1. IntroductionOpen in SageMaker Studio Lab
ch2. Probability DistributionsOpen in SageMaker Studio Lab
ch3. Linear Models for RegressionOpen in SageMaker Studio Lab
ch4. Linear Models for ClassificationOpen in SageMaker Studio Lab
ch5. Neural NetworksOpen in SageMaker Studio Lab
ch6. Kernel MethodsOpen in SageMaker Studio Lab
ch7. Sparse Kernel MachinesOpen in SageMaker Studio Lab
ch8. Graphical ModelsOpen in SageMaker Studio Lab
ch9. Mixture Models and EMOpen in SageMaker Studio Lab
ch10. Approximate InferenceOpen in SageMaker Studio Lab
ch11. Sampling MethodsOpen in SageMaker Studio Lab
ch12. Continuous Latent VariablesOpen in SageMaker Studio Lab
ch13. Sequential DataOpen in SageMaker Studio Lab

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