{"id":15621675,"url":"https://github.com/gerdm/prml","last_synced_at":"2025-05-15T13:04:16.589Z","repository":{"id":37330957,"uuid":"158837265","full_name":"gerdm/prml","owner":"gerdm","description":"Repository of notes, code and notebooks in Python for the book Pattern Recognition and Machine Learning by Christopher Bishop","archived":false,"fork":false,"pushed_at":"2022-07-25T04:36:22.000Z","size":33921,"stargazers_count":2303,"open_issues_count":1,"forks_count":516,"subscribers_count":36,"default_branch":"master","last_synced_at":"2025-04-07T16:12:36.544Z","etag":null,"topics":["bayesian-statistics","machine-learning","pattern-recognition","prml","python"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"agpl-3.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/gerdm.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE.rst","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2018-11-23T13:35:29.000Z","updated_at":"2025-04-07T12:53:37.000Z","dependencies_parsed_at":"2022-07-06T08:33:26.079Z","dependency_job_id":null,"html_url":"https://github.com/gerdm/prml","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/gerdm%2Fprml","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/gerdm%2Fprml/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/gerdm%2Fprml/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/gerdm%2Fprml/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/gerdm","download_url":"https://codeload.github.com/gerdm/prml/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":248961186,"owners_count":21189991,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["bayesian-statistics","machine-learning","pattern-recognition","prml","python"],"created_at":"2024-10-03T09:51:27.411Z","updated_at":"2025-04-14T20:57:06.986Z","avatar_url":"https://github.com/gerdm.png","language":"Jupyter Notebook","readme":"# Pattern Recognition and Machine Learning (PRML)\n\n![MDN](https://i.imgur.com/2uCUY3q.png)\n\n[![nbviewer](https://raw.githubusercontent.com/jupyter/design/master/logos/Badges/nbviewer_badge.svg)](https://nbviewer.jupyter.org/github/gerdm/prml/tree/master/)\n\n\nThis 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.\n\n## Discussions (new)\nIf you have any questions and/or requests, check out the [discussions](https://github.com/gerdm/prml/discussions) page!\n\n## Useful Links\n* [PRML Book](https://www.microsoft.com/en-us/research/publication/pattern-recognition-machine-learning)\n* [Matrix Calculus](http://www.matrixcalculus.org/matrixCalculus)\n* [The Matrix Cookbook](https://www.math.uwaterloo.ca/~hwolkowi/matrixcookbook.pdf)\n* [PRML Errata](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/05/prml-errata-1st-20110921.pdf)\n* [More PRML Errata (repo)](https://github.com/yousuketakada/prml_errata)\n\n## Content\n```\n.\n├── README.md\n├── chapter01\n│   ├── einsum.ipynb\n│   ├── exercises.ipynb\n│   └── introduction.ipynb\n├── chapter02\n│   ├── Exercises.ipynb\n│   ├── bayes-binomial.ipynb\n│   ├── bayes-normal.ipynb\n│   ├── density-estimation.ipynb\n│   ├── exponential-family.ipynb\n│   ├── gamma-distribution.ipynb\n│   ├── mixtures-of-gaussians.ipynb\n│   ├── periodic-variables.ipynb\n│   ├── robbins-monro.ipynb\n│   └── students-t-distribution.ipynb\n├── chapter03\n│   ├── bayesian-linear-regression.ipynb\n│   ├── equivalent-kernel.ipynb\n│   ├── evidence-approximation.ipynb\n│   ├── linear-models-for-regression.ipynb\n│   ├── ml-vs-map.ipynb\n│   ├── predictive-distribution.ipynb\n│   └── sequential-bayesian-learning.ipynb\n├── chapter04\n│   ├── exercises.ipynb\n│   ├── fisher-linear-discriminant.ipynb\n│   ├── least-squares-classification.ipynb\n│   ├── logistic-regression.ipynb\n│   └── perceptron.ipynb\n├── chapter05\n│   ├── backpropagation.ipynb\n│   ├── bayesian-neural-networks.ipynb\n│   ├── ellipses.ipynb\n│   ├── imgs\n│   │   └── f51.png\n│   ├── mixture-density-networks.ipynb\n│   ├── soft-weight-sharing.ipynb\n│   └── weight-space-symmetry.ipynb\n├── chapter06\n│   ├── gaussian-processes.ipynb\n│   └── kernel-regression.ipynb\n├── chapter07\n│   ├── relevance-vector-machines.ipynb\n│   └── support-vector-machines.ipynb\n├── chapter08\n│   ├── exercises.ipynb\n│   ├── graphical-model-inference.ipynb\n│   ├── img.jpeg\n│   ├── markov-random-fields.ipynb\n│   ├── sum-product.ipynb\n│   └── trees.ipynb\n├── chapter09\n│   ├── gaussian-mixture-models.ipynb\n│   ├── k-means.ipynb\n│   └── mixture-of-bernoulli.ipynb\n├── chapter10\n│   ├── exponential-mixture-gaussians.ipynb\n│   ├── local-variational-methods.ipynb\n│   ├── mixture-gaussians.ipynb\n│   ├── variational-logistic-regression.ipynb\n│   └── variational-univariate-gaussian.ipynb\n├── chapter11\n│   ├── adaptive-rejection-sampling.ipynb\n│   ├── gibbs-sampling.ipynb\n│   ├── hybrid-montecarlo.ipynb\n│   ├── markov-chain-motecarlo.ipynb\n│   ├── rejection-sampling.ipynb\n│   ├── slice-sampling.ipynb\n│   └── transformation-random-variables.ipynb\n├── chapter12\n│   ├── bayesian-pca.ipynb\n│   ├── kernel-pca.ipynb\n│   ├── ppca.py\n│   ├── principal-component-analysis.ipynb\n│   └── probabilistic-pca.ipynb\n├── chapter13\n│   ├── em-hidden-markov-model.ipynb\n│   ├── hidden-markov-model.ipynb\n│   └── linear-dynamical-system.ipynb\n├── chapter14\n│   ├── CART.ipynb\n│   ├── boosting.ipynb\n│   ├── cmm-linear-regression.ipynb\n│   ├── cmm-logistic-regression.ipynb\n│   └── tree.py\n└── misc\n    └── tikz\n        ├── ch13-hmm.tex\n        └── ch8-sum-product.tex\n\n17 directories, 73 files\n```\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgerdm%2Fprml","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fgerdm%2Fprml","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgerdm%2Fprml/lists"}