{"id":19068731,"url":"https://github.com/machine-learning-tokyo/ml-math","last_synced_at":"2025-04-28T13:41:18.832Z","repository":{"id":96775738,"uuid":"234677013","full_name":"Machine-Learning-Tokyo/ML-Math","owner":"Machine-Learning-Tokyo","description":"Mathematics for Machine Learning","archived":false,"fork":false,"pushed_at":"2020-09-15T20:51:37.000Z","size":3575,"stargazers_count":29,"open_issues_count":0,"forks_count":5,"subscribers_count":7,"default_branch":"master","last_synced_at":"2025-04-18T16:27:00.042Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"CSS","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Machine-Learning-Tokyo.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2020-01-18T03:41:56.000Z","updated_at":"2023-09-22T15:20:56.000Z","dependencies_parsed_at":null,"dependency_job_id":"51952e37-8ce0-4a53-ac29-a32824ca5f84","html_url":"https://github.com/Machine-Learning-Tokyo/ML-Math","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/Machine-Learning-Tokyo%2FML-Math","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Machine-Learning-Tokyo%2FML-Math/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Machine-Learning-Tokyo%2FML-Math/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Machine-Learning-Tokyo%2FML-Math/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Machine-Learning-Tokyo","download_url":"https://codeload.github.com/Machine-Learning-Tokyo/ML-Math/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":251321233,"owners_count":21570700,"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":[],"created_at":"2024-11-09T01:11:35.522Z","updated_at":"2025-04-28T13:41:18.813Z","avatar_url":"https://github.com/Machine-Learning-Tokyo.png","language":"CSS","readme":"# ML-Math\n\nThis repository is part of our **MLT もくもく会 Math Reading Sessions** and the **MLT Mathematics for Machine Learning Discussions**. \n\n\n# Review sessions and presentations\n\n### [YouTube Playlist](https://www.meetup.com/Machine-Learning-Tokyo/events/270761078/)\n\n\n- [Singular Value Decomposition](https://youtu.be/ONScfggC-M0) by [Jayson Cunanan, Ph.D.](https://www.linkedin.com/in/jayson-cunanan-phd/), AI Researcher/Engineer at AI inside 株式会社\n- [Intro to Principal Component Analysis and Probabilistic PCA](https://www.youtube.com/watch?v=yyO08F5bFuA\u0026list=PLaPdEEY26UXygpV-Cxch8Xkpl7IbFKBvK\u0026index=5\u0026t=0s) by [Hiroshi Urata, Data Scientist](https://www.linkedin.com/in/hiroshi-u/), Data Scientist, IBM Japan\n- [Fourier transforms and a brief comparison with SVD](https://www.youtube.com/watch?v=8zRmr25vYBw\u0026list=PLaPdEEY26UXygpV-Cxch8Xkpl7IbFKBvK\u0026index=4\u0026t=0s) by [Jayson Cunanan, Ph.D.](https://www.linkedin.com/in/jayson-cunanan-phd/), AI Researcher/Engineer at AI inside 株式会社\n- [ML Math Review Session: Singular Value Decomposition](https://www.youtube.com/watch?v=PrIv-7SBTw8\u0026list=PLaPdEEY26UXygpV-Cxch8Xkpl7IbFKBvK\u0026index=3\u0026t=0s) by [Emil Vatai](https://twitter.com/vatai), Postdoctoral Researcher, RIKEN, Japan (Review Chapter 4: Matrix Decompositions)\n- [ML Math Review Session: Groups, residue classes](https://www.youtube.com/watch?v=nOxQ1vRt_p0\u0026list=PLaPdEEY26UXygpV-Cxch8Xkpl7IbFKBvK\u0026index=2\u0026t=0s) by [Emil Vatai](https://twitter.com/vatai), Postdoctoral Researcher, RIKEN, Japan (Review Chapter 2: Linear Algebra)\n- [ML Math Review Session: Gaussian Mixture Models](https://www.youtube.com/watch?v=2Tw0peN814k\u0026t=10s) by [Pavitra Chakravarty](https://twitter.com/genomixgmailcom), Data Analyst, Converging Health, Dallas, TX USA (Review Chapter 11: Density Estimation with Gaussian Mixture Models)\n\n\n# Reading sessions\n\nOur [もくもく会 ML Math Reading Sessions](https://machinelearningtokyo.com/2019/11/28/ml-math-reading-sessions/) were based on \"Mathematics For Machine Learning\" by Marc Peter Deisenroth, A Aldo Faisal, and Cheng Soon Ong, to be published by Cambridge University Press. https://mml-book.github.io/\n\nSessions were held bi-weekly in different time zones on Sundays (PST, EST, GMT, CET, IST) and Mondays (APAC).\n\n\n## Part I: Mathematical Foundations\n\n- Introduction and Motivation\n- Linear Algebra\n- Analytic Geometry\n- Matrix Decompositions\n- Vector Calculus\n- Probability and Distribution\n- Continuous Optimization\n\n## Part II: Central Machine Learning Problems\n\n- When Models Meet Data\n- Linear Regression\n- Dimensionality Reduction with Principal Component Analysis\n- Density Estimation with Gaussian Mixture Models\n- Classification with Support Vector Machines\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmachine-learning-tokyo%2Fml-math","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmachine-learning-tokyo%2Fml-math","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmachine-learning-tokyo%2Fml-math/lists"}