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https://github.com/gerdm/prml
Repository of notes, code and notebooks in Python for the book Pattern Recognition and Machine Learning by Christopher Bishop
https://github.com/gerdm/prml
bayesian-statistics machine-learning pattern-recognition prml python
Last synced: 23 days ago
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Repository of notes, code and notebooks in Python for the book Pattern Recognition and Machine Learning by Christopher Bishop
- Host: GitHub
- URL: https://github.com/gerdm/prml
- Owner: gerdm
- License: agpl-3.0
- Created: 2018-11-23T13:35:29.000Z (almost 6 years ago)
- Default Branch: master
- Last Pushed: 2022-07-25T04:36:22.000Z (over 2 years ago)
- Last Synced: 2024-10-04T09:51:25.173Z (about 1 month ago)
- Topics: bayesian-statistics, machine-learning, pattern-recognition, prml, python
- Language: Jupyter Notebook
- Homepage:
- Size: 32.3 MB
- Stars: 2,105
- Watchers: 33
- Forks: 488
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE.rst
Awesome Lists containing this project
README
# Pattern Recognition and Machine Learning (PRML)
[](https://nbviewer.jupyter.org/github/gerdm/prml/tree/master/)
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.
## Discussions (new)
If you have any questions and/or requests, check out the [discussions](https://github.com/gerdm/prml/discussions) page!
## Useful Links
* [PRML Book](https://www.microsoft.com/en-us/research/publication/pattern-recognition-machine-learning)
* [Matrix Calculus](http://www.matrixcalculus.org/matrixCalculus)
* [The Matrix Cookbook](https://www.math.uwaterloo.ca/~hwolkowi/matrixcookbook.pdf)
* [PRML Errata](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/05/prml-errata-1st-20110921.pdf)
* [More PRML Errata (repo)](https://github.com/yousuketakada/prml_errata)
## 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
```