{"id":961,"date":"2024-10-04T11:21:34","date_gmt":"2024-10-04T15:21:34","guid":{"rendered":"https:\/\/ozer.gt\/log\/?p=961"},"modified":"2024-10-04T11:21:34","modified_gmt":"2024-10-04T15:21:34","slug":"mathematical-methods-in-data-science-with-python","status":"publish","type":"post","link":"https:\/\/ozer.gt\/log\/2024\/10\/04\/mathematical-methods-in-data-science-with-python\/","title":{"rendered":"Mathematical Methods in Data Science (with Python)"},"content":{"rendered":"<p>Just came across this neat resource while looking for an MCMC \/ Gibbs sampling code example in object recognition. Self-description of the book:<\/p>\n<blockquote><p><em>This textbook on the mathematics of data has two intended audiences:<\/em><\/p>\n<ul>\n<li><em>For students majoring in math or other quantitative fields like physics, economics, engineering, etc.: it is meant as an invitation to data science and AI from a rigorous mathematical perspective.<\/em><\/li>\n<li><em>For mathematically-inclined students in data science related fields (at the undergraduate or graduate level): it can serve as a mathematical companion to machine learning, AI, and statistics courses.<\/em><\/li>\n<\/ul>\n<\/blockquote>\n<p>Not yet published, but you can check it out <a href=\"https:\/\/mmids-textbook.github.io\/index.html\">here<\/a>.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Just came across this neat resource while looking for an MCMC \/ Gibbs sampling code example in object recognition. Self-description of the book: This textbook on the mathematics of data has two intended audiences: For students majoring in math or other quantitative fields like physics, economics, engineering, etc.: it is meant as an invitation to [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"cybocfi_hide_featured_image":"","footnotes":""},"categories":[1],"tags":[],"class_list":["post-961","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"_links":{"self":[{"href":"https:\/\/ozer.gt\/log\/wp-json\/wp\/v2\/posts\/961","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/ozer.gt\/log\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/ozer.gt\/log\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/ozer.gt\/log\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/ozer.gt\/log\/wp-json\/wp\/v2\/comments?post=961"}],"version-history":[{"count":1,"href":"https:\/\/ozer.gt\/log\/wp-json\/wp\/v2\/posts\/961\/revisions"}],"predecessor-version":[{"id":962,"href":"https:\/\/ozer.gt\/log\/wp-json\/wp\/v2\/posts\/961\/revisions\/962"}],"wp:attachment":[{"href":"https:\/\/ozer.gt\/log\/wp-json\/wp\/v2\/media?parent=961"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/ozer.gt\/log\/wp-json\/wp\/v2\/categories?post=961"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/ozer.gt\/log\/wp-json\/wp\/v2\/tags?post=961"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}