{"id":19179215,"url":"https://github.com/melling/islr","last_synced_at":"2025-05-07T21:45:52.742Z","repository":{"id":119865371,"uuid":"317629242","full_name":"melling/ISLR","owner":"melling","description":"Introduction to Statistical Learning","archived":false,"fork":false,"pushed_at":"2021-03-18T20:45:25.000Z","size":4864,"stargazers_count":61,"open_issues_count":0,"forks_count":10,"subscribers_count":3,"default_branch":"main","last_synced_at":"2025-05-07T21:45:47.066Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"R","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/melling.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":"2020-12-01T18:16:58.000Z","updated_at":"2025-04-06T15:38:51.000Z","dependencies_parsed_at":null,"dependency_job_id":"a20ea020-390f-4bc5-84ac-ad85952b6333","html_url":"https://github.com/melling/ISLR","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/melling%2FISLR","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/melling%2FISLR/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/melling%2FISLR/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/melling%2FISLR/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/melling","download_url":"https://codeload.github.com/melling/ISLR/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":252961858,"owners_count":21832192,"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":"2024-11-09T10:42:28.473Z","updated_at":"2025-05-07T21:45:52.713Z","avatar_url":"https://github.com/melling.png","language":"R","funding_links":[],"categories":[],"sub_categories":[],"readme":"# ISLR\nIntroduction to Statistical Learning: http://www.StatLearning.com\n\nThe PDF book is available for free on the author's site above. \n\n\nWorking through the book and the labs.\n\n[Videos](https://www.r-bloggers.com/2014/09/in-depth-introduction-to-machine-learning-in-15-hours-of-expert-videos/)\n\nOur solutions:\n\n- [Chapter 2 Solutions](chapter02)\n- [Chapter 3 Solutions](chapter03)\n- [Chapter 4 Solutions](chapter04)\n- [Chapter 5 Solutions](chapter05)\n- [Chapter 6 Solutions](chapter06)\n- [Chapter 7 Solutions](chapter07)\n- [Chapter 8 Solutions](chapter08)\n- [Chapter 9 Solutions](chapter09)\n- [Chapter 10 Solutions](chapter10)\n\n## Course Slides for Videos\n\n- [Chapter 1: Introduction ](https://web.stanford.edu/~hastie/MOOC-Slides/introduction.pdf)\n- [Chapter 2: Statistical Learning ](https://web.stanford.edu/~hastie/MOOC-Slides/statistical_learning.pdf)\n- [Chapter 3: Linear Regression ](https://web.stanford.edu/~hastie/MOOC-Slides/linear_regression.pdf)\n- [Chapter 4: Classification ](https://web.stanford.edu/~hastie/MOOC-Slides/classification.pdf)\n- [Chapter 5: Resampling Methods ](https://web.stanford.edu/~hastie/MOOC-Slides/cv_boot.pdf)\n- [Chapter 6: Linear Model Selection and Regularization ](https://web.stanford.edu/~hastie/MOOC-Slides/model_selection.pdf)\n- [Chapter 7: Moving Beyond Linearity ](https://web.stanford.edu/~hastie/MOOC-Slides/nonlinear.pdf)\n- [Chapter 8: Tree-Based Methods ](https://web.stanford.edu/~hastie/MOOC-Slides/trees.pdf)\n- [Chapter 9: Support Vector Machines ](https://web.stanford.edu/~hastie/MOOC-Slides/svm.pdf)\n- [Chapter 10: Unsupervised Learning](https://web.stanford.edu/~hastie/MOOC-Slides/unsupervised.pdf)\n\n## Other Solutions\n\n- http://blog.princehonest.com/stat-learning/\n    - https://github.com/asadoughi/stat-learning/\n- https://github.com/ppaquay/IntroStatLearning\n- https://github.com/jilmun/ISLR\n- https://github.com/KamranMK/ISLR-Solutions\n- https://laptrinhx.com/solutions-to-labs-and-excercises-from-an-introduction-to-statistical-learning-as-jupyter-notebooks-1723363054/\n- https://altaf-ali.github.io/ISLR/index.html\n- https://github.com/evertonjlima/Notebooks\n- https://waxworksmath.com/Authors/G_M/James/james.html\n\n## Python Solutions\n\n- https://github.com/tdpetrou/Machine-Learning-Books-With-Python/tree/master/Introduction%20to%20Statistical%20Learning\n- https://www.franzoni.eu/machine-learning-a-sound-primer/\n- https://github.com/JWarmenhoven/ISLR-python\n\n## Julia Solutions\n\n- https://github.com/tndoan/ISLR.jl\n\n# Machine Learning Articles\n\n- [The Matrix Calculus You Need For Deep Learning](https://explained.ai/matrix-calculus/index.html)\n- [Deriving convolution from first principles](https://towardsdatascience.com/deriving-convolution-from-first-principles-4ff124888028)\n\n# Other Math Introductory Books\n\n- http://www.cs.cmu.edu/~zkolter/course/linalg/linalg_notes.pdf\n- http://joshua.smcvt.edu/linearalgebra/\n- http://vmls-book.stanford.edu/\n   - https://ses.library.usyd.edu.au/handle/2123/21370\n- https://projects.iq.harvard.edu/stat110/home\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmelling%2Fislr","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmelling%2Fislr","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmelling%2Fislr/lists"}