{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/introduction-to-machine-learning-class-notes","title":"Introduction to Machine Learning: Class Notes 67577","arxiv_id":"0904.3664","date":"2009-04-23","proceeding":null,"authors":["Amnon Shashua"],"abstract":"Introduction to Machine learning covering Statistical Inference (Bayes, EM,\nML/MaxEnt duality), algebraic and spectral methods (PCA, LDA, CCA, Clustering),\nand PAC learning (the Formal model, VC dimension, Double Sampling theorem).","url_abs":"http://arxiv.org/abs/0904.3664v1","url_pdf":"http://arxiv.org/pdf/0904.3664v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"introduction-to-machine-learning-class-notes","repo_url":"https://github.com/carsonzhu/Machine-learning-oriented","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"clustering","task_name":"Clustering"},{"task_slug":"pac-learning","task_name":"PAC learning"}],"methods":[{"method_slug":"lda","method_name":"LDA"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=0904.3664","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}