{"id":15655180,"url":"https://github.com/mayer79/statistical_computing_material","last_synced_at":"2025-05-01T14:54:11.497Z","repository":{"id":100787122,"uuid":"562209079","full_name":"mayer79/statistical_computing_material","owner":"mayer79","description":"Material for the lecture Statistical Computing","archived":false,"fork":false,"pushed_at":"2025-04-27T11:01:30.000Z","size":30865,"stargazers_count":8,"open_issues_count":0,"forks_count":14,"subscribers_count":3,"default_branch":"main","last_synced_at":"2025-05-01T14:54:03.084Z","etag":null,"topics":["data-science","machine-learning","r","statistics"],"latest_commit_sha":null,"homepage":"https://mayer79.github.io/statistical_computing_material/","language":"TeX","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/mayer79.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,"zenodo":null}},"created_at":"2022-11-05T16:30:02.000Z","updated_at":"2025-04-27T11:01:31.000Z","dependencies_parsed_at":"2024-02-16T23:08:40.284Z","dependency_job_id":"e7acbbb5-9c11-4893-9102-fbfb263eee8a","html_url":"https://github.com/mayer79/statistical_computing_material","commit_stats":null,"previous_names":[],"tags_count":2,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mayer79%2Fstatistical_computing_material","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mayer79%2Fstatistical_computing_material/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mayer79%2Fstatistical_computing_material/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mayer79%2Fstatistical_computing_material/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/mayer79","download_url":"https://codeload.github.com/mayer79/statistical_computing_material/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":251895424,"owners_count":21661342,"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":["data-science","machine-learning","r","statistics"],"created_at":"2024-10-03T12:56:51.243Z","updated_at":"2025-05-01T14:54:11.468Z","avatar_url":"https://github.com/mayer79.png","language":"TeX","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Statistical Computing \u003cimg src='figs/logo.png' align=\"right\" height=\"200\" /\u003e\u003c/a\u003e\n\n### Lecture Notes\n\n#### Michael Mayer\n\n## Organization\n\nThe lecture has six chapters: \n\n1. [R in Action](https://mayer79.github.io/statistical_computing_material/1_R_in_Action.html)\n2. [Statistical Inference](https://mayer79.github.io/statistical_computing_material/2_Statistical_Inference.html)\n3. [Linear Models](https://mayer79.github.io/statistical_computing_material/3_Linear_Models.html)\n4. [Model Selection and Validation](https://mayer79.github.io/statistical_computing_material/4_Model_Selection_and_Validation.html)\n5. [Trees](https://mayer79.github.io/statistical_computing_material/5_Trees.html)\n6. [Neural Nets](https://mayer79.github.io/statistical_computing_material/6_Neural_Nets.html)\n\nChapters 3 to 6 can be summarized as \"Statistical ML in Action\". \n\nEach chapter will keep us busy for two weeks (3 hours + 1 hour exercises).\n   \n## Prerequisites\n\n### Lecture material\n\nFetch everything by running\n\n```\ngit clone https://github.com/mayer79/statistical_computing_material.git\n```\n\nin your Git console, or by downloading everything as Zip file.\n\n### Large data\n\nDownload the large dataset \"January 2018 - Yellow Taxi Trip Records\" from [this page](https://www1.nyc.gov/site/tlc/about/tlc-trip-record-data.page).\n\nPlace it in the project subfolder \"taxi/\".\n\n### Software\n\nWe will work with R version \u003e= 4.4 and RStudio. \n\nIn the first two chapters, we will need these contributed R packages:\n\n- tidyverse\n- plotly\n- insuranceData\n- bench\n- withr\n- boot\n- coin\n\nFor the remaining chapters, we further need:\n\n- h2o (requires Java)\n- arrow\n- data.table\n- FNN\n- duckdb\n- sparklyr (requires Java)\n- rpart.plot\n- ranger\n- xgboost\n- lightgbm\n- hstats\n- MetricsWeighted\n- effectplots\n- keras (requires Python, see below)\n\nFor the last chapter, we additionally need Python with TensorFlow \u003e= 2.15. You can install it by running the R command `keras::install_keras(version = \"release-cpu\")`. If the following code works, you are all set. (Some red start-up messages/warnings are okay.)\n\n```\nlibrary(tensorflow)\ntf$constant(\"Hello Tensorflow!\")\n```\n\n## Further Material\n\n### Books\n\n- James, G., Witten, D., Hastie, T., Tibshirani, R. (2013). *An Introduction to Statistical Learning - with Applications in R*. New York: Springer.\n- Hastie, T., Tibshirani, R., Friedman, J. (2001). *The Elements of Statistical Learning: Data Mining, Inference, and Prediction*. New York: Springer.\n- Wickham, H., Grolemund, G. (2017). *R for Data Science: Import, Tidy, Transform, Visualize, and Model Data*. O'Reilly Media.\n- Chollet, F., Allaire, J. J. (2018). *Deep Learning with R*. Manning Publications Co.\n\n### Video by Trevor Hastie\n\n- Hastie Big Data 45': https://www.youtube.com/watch?v=0EWJZIC4JxA\n\n## Copyright\n\nThis lecture is being distributed under the [creative commons license](https://creativecommons.org/licenses/by/2.0/).\n\n## How to cite?\n\nMichael Mayer (2025), *Statistical Computing*, lecture notes, Institute of Mathematical Statistics and Actuarial Science, University of Bern. URL:  [https://github.com/mayer79/statistical_computing_material](https://github.com/mayer79/statistical_computing_material)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmayer79%2Fstatistical_computing_material","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmayer79%2Fstatistical_computing_material","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmayer79%2Fstatistical_computing_material/lists"}