{"store":{"baseInfo":{"podcastTitle":"Data Science Decoded","slug":"datasciencedecoded","podcastDescription":"We discuss seminal mathematical papers (part of them are really old 😎 ) that have shaped and fundamentally established the fields of machine learning and data science as we know them today. \n\nThe goal of the podcast is to introduce you (and us) to the evolution of these fields from a mathematical and sometimes slightly philosophical perspective. \n\n\nWe will discuss the contribution of these papers, not just from a pure mathematical aspect but also how they influenced the discourse in the field, which areas were opened up as a result, and so on.\n\nOur podcast episodes are also available on our youtube channel: you are invited to listen, subscribe, follow, and most importantly, correct the mistakes we have probably already made and will make in the future: \n\nhttps://youtu.be/wThcXx_vXjQ?si=vnMfsddZGq3MsaJ2\n\nOur podcast website: https://datasciencedecodedpodcast.com/","podcastLogo":"https://pbcdn1.podbean.com/imglogo/image-logo/18630391/Resized_DSD_Logo_3000_efnsaw.jpg","podcastShareLogo":"https://d2bwo9zemjwxh5.cloudfront.net/image-logo/18630391/Resized_DSD_Logo_3000_efnsaw_1200x628.jpg?s=34b2c172cbdb3197ff7237525bf61308&e=jpg","podcastUrl":"https://datasciencedecoded.podbean.com","podcastCanSearched":true,"feedUrl":"https://feed.podbean.com/datasciencedecoded/feed.xml","theme":"elegant","podcastDeepLink":"https://www.podbean.com/pu/pbblog-r2fmu-11c46f7","removePodbeanBranding":"no","brandingLogo":null,"timeZone":{"timeZoneId":17,"gmtAdjustment":"GMT-04:00","useDaylightTime":1,"value":-4,"text":"(GMT-04:00) Atlantic Time (Canada)","daylightStartTime":"Second Sunday Of March +2 Hours","daylightEndTime":"First Sunday Of November +2 Hours","timezone":"America/Halifax"},"podcastDownloads":null,"totalEpisodes":2,"showComments":true,"iOSAppDownloadLink":null,"customizeCssUrl":null,"androidAppDownloadLink":null,"showPostShare":true,"showLike":true,"author":"Mike Erlihson","engagementStatsBaseUrl":null,"shareEmbedLink":"https://admin5.podbean.com/embed.html?id=pbblog-r2fmu-11c46f7-playlist","language":"en","sensitivitySessionExpireTime":3600,"podcastApps":[{"name":"Podbean App","link":"https://r4j68.app.goo.gl/?link=https%3A%2F%2Fdatasciencedecoded.podbean.com%3Faction%3DopenPodcast%26podcastId%3Dpbblog18630391%26podcastIdTag%3Dr2fmu&dfl=https%3A%2F%2Fdatasciencedecoded.podbean.com&utm_campaign=pb_web&utm_source=podbean_web&utm_medium=dlink&imv=670&amv=530&isi=973361050&ibi=com.podbean.app.podcast&ipbi=com.podbean.app.podcast&apn=com.podbean.app.podcast","image":"https://pbcdn1.podbean.com/fs1/site/images/admin5/podbean-app.png","title":null},{"name":"Spotify","link":"https://open.spotify.com/show/1SSdryTTXlCi7DKJBiJRWa","image":"https://pbcdn1.podbean.com/fs1/site/images/admin5/spotify.png","title":null},{"name":"Samsung","link":null,"image":"https://pbcdn1.podbean.com/fs1/site/images/admin5/Samsung.png","title":"Search the podcast title on Samsung Podcast app."}],"components":{"base":[{"id":"MainStyle","val":"light"},{"id":"Font","val":"Avenir"},{"id":"SecondaryFont","val":"Avenir"},{"id":"PlayerColor","val":"#8fc31f"},{"id":"PlayerStyle","val":"player1"},{"id":"PlayerWave","val":"show"},{"id":"Favicon","val":null},{"id":"CookiesNotice","val":"hide"},{"id":"CookiesNoticeContent","val":"

Cookies Notice

This website uses cookies to improve your experience while you navigate through the website. By using our website you agree to our use of cookies.

"}],"home-header":[{"id":"HomeHeaderTextColor","val":"#000000"},{"id":"HomeHeaderBackgroundColor","val":"#ffffff"}],"page-header":[{"id":"PageHeaderTextColor","val":"#ffffff"},{"id":"PageHeaderBackgroundColor","val":"#868686"}],"episode-list":[{"id":"EpisodeListType","val":"grid"},{"id":"EpisodesPerPage","val":10},{"id":"EpisodeStats","val":"show"}],"navigation":[{"id":"NavigationFontColor","val":"#000000"},{"id":"NavigationBackgroundColor","val":"#e9e9e9"},{"id":"NavigationSearch","val":"show"},{"id":"NavigationLinks","val":[{"id":"Home","type":"Link","value":"/","text":"Home","subitems":[]},{"id":"Subscribe","type":"Link","value":"https://www.podbean.com/site/podcatcher/index/blog/oA8gOqi0purw","text":"Subscribe","subitems":[]},{"id":"Profile","type":"Link","value":"https://www.podbean.com/user-r15zonsvRuqN","text":"Profile","subitems":[]}]}],"footer":[{"id":"FooterType","val":"Footer2"},{"id":"FooterFontColor","val":"#ffffff"},{"id":"FooterBackgroundColor","val":"#000000"},{"id":"FooterCopyright","val":"Copyright 2024 All rights reserved."},{"id":"SocialMediaLinks","val":{"facebook_username":"","twitter_username":"","instagram_username":"","youtube_username":"","linkedin_username":""}}],"customize-container1":[],"customize-container2":[{"id":"ComponentImageText-1","val":{"ComponentFontColor":"#000000","ComponentBackgroundColor":"#f2f2f2","ComponentImage":"https://mcdn.podbean.com/mf/web/3t450rwaiz6ktn8w/aa.jpg","ComponentContent":"

Data Science Decoded(Fully)

The Data Science Decoded Podcast explores seminal mathematical papers that have significantly influenced the fields of machine learning and data science.

The podcast aims to bridge historical mathematical methods with modern practices, emphasizing the evolution of statistical concepts and their impact on contemporary data science.

Hosted by experienced professionals, Daniel Aronovich, Dr. Mike Erlihson and Dr. Nir Regev, the episodes provide insights into the foundational works and their relevance to current methodologies. The content is available on various platforms, including Spotify, Apple Podcasts, and YouTube.

For more information, visit [Data Science Decoded Podcast](https://datasciencedecodedpodcast.com/).

"}}]},"allLinks":[],"mainStylesDefaultSettings":null,"defaultCustomizeComponents":null,"scripts":[],"metas":null,"appConfig":{"package":"com.podbean.app.podcast","app_store_id":973361050,"app_name":"Podbean","scheme":"podbean"},"customizeCss":null,"canEditCss":null,"enableJsCodeInCustomizeContent":false,"enabledIframeCodeInCustomizeContent":true,"sectionMaxCount":10,"enableEditorLink":true,"enableEditCookiesNotice":true,"siteRedirectToFeed":false,"podcatcherRssUrl":null,"showFeed":true,"showTopBar":true,"enableWebSiteLike":null,"organizationName":null,"organizationPageLink":null},"backToListUrl":"/","tinymceLoaded":false,"listEpisodes":[],"userLikedEpisodes":[],"topBarIframeLoaded":false,"listTotalCount":0,"listTotalPage":0,"listPage":0,"listType":"all","listValue":"","listName":"","pageLoading":false,"apiBaseUrl":"https://datasciencedecoded.podbean.com","apiCommonParams":{"t":"67227a59","clientSignature":"00ccecab77b8af651f55f28973503d3f","u":null,"customizing":null},"apiCommonHeaders":{},"comments":[],"commentsTotalCount":0,"commentsTotalPage":0,"commentsPage":1,"commentEpisodeId":"pb23399340-3ah5z","episode":{"id":"pb23399340","idTag":"3ah5z","title":"Data Science Decoded - Fisher RA. \"On the mathematical foundations of theoretical statistics\"(1922)","logo":"https://pbcdn1.podbean.com/imglogo/ep-logo/pbblog18630391/Data_Science_Decoded_jtektz_300x300.png","shareLogo":"https://d2bwo9zemjwxh5.cloudfront.net/ep-logo/pbblog18630391/Data_Science_Decoded_jtektz_1200x628.jpg?s=f5bdf63bb54439f47ae935023ffdfaa1&e=png","largeLogo":"https://pbcdn1.podbean.com/imglogo/ep-logo/pbblog18630391/Data_Science_Decoded_jtektz.png","previewContent":null,"content":"

We discuss Ronald A. Fisher's paper \"On the Mathematical Foundations of Theoretical Statistics\"(1922), which profoundly shaped the field of statistics by establishing key concepts such as maximum likelihood estimation, which is crucial for parameter estimation in statistical models.

","publishTimestamp":1719389036,"duration":4625,"mediaUrl":"https://mcdn.podbean.com/mf/web/zv84ipff64qe52mw/Data_Science_Decoded_-_Fisher_RA_On_the_mathematical_foundations_of_theoretical_statistics_1922_8jiuy.mp3","mediaType":"audio/mpeg","shareLink":"https://admin5.podbean.com/embed.html?id=pb-3ah5z-1650bac","likedCount":0,"deepLink":"https://www.podbean.com/ew/pb-3ah5z-1650bac","transcriptUrl":null,"permalink":"/e/fisher_on_the_math_foundations_of_theoretical_statistics/","downloadLink":"https://www.podbean.com/site/EpisodeDownload/PB1650BAC3AH5Z","fallbackResource":null,"fallbackResourceMimetype":null,"downloadCount":138,"status":"publish","hasMoreContent":false,"chaptersUrl":null},"episodeShareLink":null,"deepLink":"https://www.podbean.com/ew/pb-3ah5z-1650bac","playlist":{"episodes":[],"current":0,"start":0,"stop":0,"playing":true,"playerLoading":true,"playbackRate":1,"playbackRateIndex":1},"toasts":[],"confirm":null,"customizeEditorFormZIndex":999,"lastRequestTime":null,"validateChangesOnBeforeunload":true,"windowWidth":1000,"VITE_FS1_CDN_URL_PREFIX":"https://pbcdn1.podbean.com/fs1","reloadStats":false},"episode":{"id":"pb23399340","idTag":"3ah5z","title":"Data Science Decoded - Fisher RA. \"On the mathematical foundations of theoretical statistics\"(1922)","logo":"https://pbcdn1.podbean.com/imglogo/ep-logo/pbblog18630391/Data_Science_Decoded_jtektz_300x300.png","shareLogo":"https://d2bwo9zemjwxh5.cloudfront.net/ep-logo/pbblog18630391/Data_Science_Decoded_jtektz_1200x628.jpg?s=f5bdf63bb54439f47ae935023ffdfaa1&e=png","largeLogo":"https://pbcdn1.podbean.com/imglogo/ep-logo/pbblog18630391/Data_Science_Decoded_jtektz.png","previewContent":null,"content":"

Data Science Decoded - Fisher RA. "On the mathematical foundations of theoretical statistics"(1922)

Comments (0)