{"id":13934782,"url":"https://github.com/rasbt/stat479-machine-learning-fs18","last_synced_at":"2025-04-05T13:09:19.756Z","repository":{"id":40531112,"uuid":"147595195","full_name":"rasbt/stat479-machine-learning-fs18","owner":"rasbt","description":"Course material for STAT 479: Machine Learning (FS 2018) at University Wisconsin-Madison","archived":false,"fork":false,"pushed_at":"2018-12-20T23:45:21.000Z","size":57749,"stargazers_count":493,"open_issues_count":0,"forks_count":229,"subscribers_count":48,"default_branch":"master","last_synced_at":"2025-03-29T12:09:35.294Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":"http://stat.wisc.edu/~sraschka/teaching/stat479-fs2018/","language":"Jupyter Notebook","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/rasbt.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}},"created_at":"2018-09-06T00:30:56.000Z","updated_at":"2025-01-08T00:33:50.000Z","dependencies_parsed_at":"2022-07-23T14:09:13.505Z","dependency_job_id":null,"html_url":"https://github.com/rasbt/stat479-machine-learning-fs18","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/rasbt%2Fstat479-machine-learning-fs18","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rasbt%2Fstat479-machine-learning-fs18/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rasbt%2Fstat479-machine-learning-fs18/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rasbt%2Fstat479-machine-learning-fs18/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/rasbt","download_url":"https://codeload.github.com/rasbt/stat479-machine-learning-fs18/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":247339158,"owners_count":20923014,"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":"2024-08-07T23:01:13.919Z","updated_at":"2025-04-05T13:09:19.738Z","avatar_url":"https://github.com/rasbt.png","language":"Jupyter Notebook","funding_links":[],"categories":["Jupyter Notebook"],"sub_categories":[],"readme":"# STAT479: Machine Learning (Fall 2018)\n\nInstructor: Sebastian Raschka\n\nLecture material for the Machine Learning course (STAT 479) at University Wisconsin-Madison. For details, please see the course website at http://pages.stat.wisc.edu/~sraschka/teaching/stat479-fs2018/\n\n\n\n**Part I: Introduction**\n\n- [Lecture 1](01_overview): What is Machine Learning? An Overview.\n- [Lecture 2](02_knn): Intro to Supervised Learning: KNN\n\n**Part II: Computational Foundations**\n\n- [Lecture 3](03_python): Using Python, Anaconda, IPython, Jupyter Notebooks\n- [Lecture 4](04_scipython): Scientific Computing with NumPy, SciPy, and Matplotlib\n- [Lecture 5](05_sklearn): Data Preprocessing and Machine Learning with Scikit-Learn\n\n**Part III: Tree-Based Methods**\n\n- [Lecture 6](06_trees): Decision Trees\n- [Lecture 7](07_ensembles): Ensemble Methods\n\n**Part IV: Evaluation**\n\n- [Lecture 8](08_eval-intro): Model Evaluation 1: Introduction to Overfitting and Underfitting\n- [Lecture 9](09_eval-ci): Model Evaluation 2: Uncertainty Estimates and Resampling\n- [Lecture 10](10_eval-cv): Model Evaluation 3: Model Selection and Cross-Validation\n- [Lecture 11](11_eval-algo): Model Evaluation 4: Algorithm Selection and Statistical Tests\n- [Lecture 12](12_eval-metrics): Model Evaluation 5: Performance Metrics\n\n**Part V: Dimensionality Reduction**\n\n- [Lecture 13](13_feat-sele): Feature Selection\n- [Lecture 14](14_feat-extract): Feature Extraction\n\n**Due to time constraints, the following topics could unfortunately not be covered:**\n\n**Part VI: Bayesian Learning** \n\n- Bayes Classifiers\n- Text Data \u0026 Sentiment Analysis\n- Naive Bayes Classification\n\n**Part VII:  Regression and Unsupervised Learning**\n\n- Regression Analysis\n- Clustering\n\n**The following topics will be covered at the beginning of the\nDeep Learning class next Spring.** [Tentative outline of the DL course](./other/dl-course-info.md).\n\n**Part VIII: Introduction to Artificial Neural Networks**\n\n- Perceptron\n- Adaline \u0026 Logistic Regression\n- SVM\n- Multilayer Perceptron\n\n\n\u003ca rel=\"license\" href=\"http://creativecommons.org/licenses/by-nc/4.0/\"\u003e\u003cimg alt=\"Creative Commons License\" style=\"border-width:0\" src=\"https://i.creativecommons.org/l/by-nc/4.0/88x31.png\" /\u003e\u003c/a\u003e\u003cbr /\u003eThis work is licensed under a \u003ca rel=\"license\" href=\"http://creativecommons.org/licenses/by-nc/4.0/\"\u003eCreative Commons Attribution-NonCommercial 4.0 International License\u003c/a\u003e.\n\n\n\u003cbr\u003e\n\u003cbr\u003e\n\u003cbr\u003e\n\nTeaching this class was a pleasure, and I am especially happy about how awesome the class projects turned out. Listed below are the winners of the three award categories as determined by ~210 votes. Congratulations! \n\n![](other/stat479-fs18-awards.jpg)","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frasbt%2Fstat479-machine-learning-fs18","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Frasbt%2Fstat479-machine-learning-fs18","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frasbt%2Fstat479-machine-learning-fs18/lists"}