/prml

Repository of notes, code and notebooks for the book Pattern Recognition and Machine Learning by Christopher Bishop

Primary LanguageJupyter Notebook

Pattern Recognition and Machine Learning (PRML)

MDN

nbviewer

This project contains Jupyter notebooks of many the algorithms presented in Christopher Bishop's Pattern Recognition and Machine Learning book, as well as replicas for many of the graphs presented in the book.

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If you have any questions and/or requests, check out the discussions page!

Useful Links

Content

.
├── README.md
├── chapter01
│   ├── einsum.ipynb
│   ├── exercises.ipynb
│   └── introduction.ipynb
├── chapter02
│   ├── Exercises.ipynb
│   ├── bayes-binomial.ipynb
│   ├── bayes-normal.ipynb
│   ├── density-estimation.ipynb
│   ├── exponential-family.ipynb
│   ├── gamma-distribution.ipynb
│   ├── mixtures-of-gaussians.ipynb
│   ├── periodic-variables.ipynb
│   ├── robbins-monro.ipynb
│   └── students-t-distribution.ipynb
├── chapter03
│   ├── bayesian-linear-regression.ipynb
│   ├── equivalent-kernel.ipynb
│   ├── evidence-approximation.ipynb
│   ├── linear-models-for-regression.ipynb
│   ├── ml-vs-map.ipynb
│   ├── predictive-distribution.ipynb
│   └── sequential-bayesian-learning.ipynb
├── chapter04
│   ├── exercises.ipynb
│   ├── fisher-linear-discriminant.ipynb
│   ├── least-squares-classification.ipynb
│   ├── logistic-regression.ipynb
│   └── perceptron.ipynb
├── chapter05
│   ├── backpropagation.ipynb
│   ├── bayesian-neural-networks.ipynb
│   ├── ellipses.ipynb
│   ├── imgs
│   │   └── f51.png
│   ├── mixture-density-networks.ipynb
│   ├── soft-weight-sharing.ipynb
│   └── weight-space-symmetry.ipynb
├── chapter06
│   ├── gaussian-processes.ipynb
│   └── kernel-regression.ipynb
├── chapter07
│   ├── relevance-vector-machines.ipynb
│   └── support-vector-machines.ipynb
├── chapter08
│   ├── exercises.ipynb
│   ├── graphical-model-inference.ipynb
│   ├── img.jpeg
│   ├── markov-random-fields.ipynb
│   ├── sum-product.ipynb
│   └── trees.ipynb
├── chapter09
│   ├── gaussian-mixture-models.ipynb
│   ├── k-means.ipynb
│   └── mixture-of-bernoulli.ipynb
├── chapter10
│   ├── exponential-mixture-gaussians.ipynb
│   ├── local-variational-methods.ipynb
│   ├── mixture-gaussians.ipynb
│   ├── variational-logistic-regression.ipynb
│   └── variational-univariate-gaussian.ipynb
├── chapter11
│   ├── adaptive-rejection-sampling.ipynb
│   ├── gibbs-sampling.ipynb
│   ├── hybrid-montecarlo.ipynb
│   ├── markov-chain-motecarlo.ipynb
│   ├── rejection-sampling.ipynb
│   ├── slice-sampling.ipynb
│   └── transformation-random-variables.ipynb
├── chapter12
│   ├── bayesian-pca.ipynb
│   ├── kernel-pca.ipynb
│   ├── ppca.py
│   ├── principal-component-analysis.ipynb
│   └── probabilistic-pca.ipynb
├── chapter13
│   ├── em-hidden-markov-model.ipynb
│   ├── hidden-markov-model.ipynb
│   └── linear-dynamical-system.ipynb
├── chapter14
│   ├── CART.ipynb
│   ├── boosting.ipynb
│   ├── cmm-linear-regression.ipynb
│   ├── cmm-logistic-regression.ipynb
│   └── tree.py
└── misc
    └── tikz
        ├── ch13-hmm.tex
        └── ch8-sum-product.tex

17 directories, 73 files