# 模式识别与机器学习

**Repository Path**: the-thinker/prml2

## Basic Information

- **Project Name**: 模式识别与机器学习
- **Description**: 模式识别与机器学习代码
- **Primary Language**: Unknown
- **License**: AGPL-3.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 1
- **Created**: 2024-07-30
- **Last Updated**: 2024-07-30

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Pattern Recognition and Machine Learning (PRML)

![MDN](https://i.imgur.com/2uCUY3q.png)

[![nbviewer](https://raw.githubusercontent.com/jupyter/design/master/logos/Badges/nbviewer_badge.svg)](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
```