{"id":30530,"date":"2022-10-08T06:00:00","date_gmt":"2022-10-08T13:00:00","guid":{"rendered":"https:\/\/insidebigdata.com\/?p=30530"},"modified":"2023-06-23T12:38:59","modified_gmt":"2023-06-23T19:38:59","slug":"research-highlights-pen-and-paper-exercises-in-machine-learning","status":"publish","type":"post","link":"https:\/\/insidebigdata.com\/2022\/10\/08\/research-highlights-pen-and-paper-exercises-in-machine-learning\/","title":{"rendered":"Research Highlights: Pen and Paper Exercises in Machine Learning"},"content":{"rendered":"\n

Title:<\/strong> Pen and Paper Exercises in Machine Learning<\/a><\/p>\n\n\n\n

This paper consists of a collection of (mostly) pen-and-paper exercises in machine learning. The exercises are on the following topics: linear algebra, optimization, directed graphical models, undirected graphical models, expressive power of graphical models, factor graphs and message passing, inference for hidden Markov models, model-based learning (including ICA and unnormalized models), sampling and Monte-Carlo integration, and variational inference. Highly recommended for data scientists wishing to evolve their understanding of the mathematical foundations of the field. <\/p>\n\n\n

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In this regular column we take a look at highlights for breaking research topics of the day in the areas of big data, data science, machine learning, AI and deep learning. For data scientists, it\u2019s important to keep connected with the research arm of the field in order to understand where the technology is headed. Enjoy!<\/p>\n","protected":false},"author":37,"featured_media":22835,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"footnotes":""},"categories":[115,180,67,268,56,84,1303,1],"tags":[277,933,96],"acf":[],"yoast_head":"\nResearch Highlights: Pen and Paper Exercises in Machine Learning - insideBIGDATA<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/insidebigdata.com\/2022\/10\/08\/research-highlights-pen-and-paper-exercises-in-machine-learning\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Research Highlights: Pen and Paper Exercises in Machine Learning - insideBIGDATA\" \/>\n<meta property=\"og:description\" content=\"In this regular column we take a look at highlights for breaking research topics of the day in the areas of big data, data science, machine learning, AI and deep learning. For data scientists, it\u2019s important to keep connected with the research arm of the field in order to understand where the technology is headed. 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Gutierrez is a Data Scientist with Los Angeles-based AMULET Analytics, a service division of AMULET Development Corp. He's been involved with data science and Big Data long before it came in vogue, so imagine his delight when the Harvard Business Review recently deemed \\\"data scientist\\\" as the sexiest profession for the 21st century. Previously, he taught computer science and database classes at UCLA Extension for over 15 years, and authored three computer industry books on database technology. He also served as technical editor, columnist and writer at a major computer industry monthly publication for 7 years. 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Gutierrez is a Data Scientist with Los Angeles-based AMULET Analytics, a service division of AMULET Development Corp. He's been involved with data science and Big Data long before it came in vogue, so imagine his delight when the Harvard Business Review recently deemed \"data scientist\" as the sexiest profession for the 21st century. Previously, he taught computer science and database classes at UCLA Extension for over 15 years, and authored three computer industry books on database technology. He also served as technical editor, columnist and writer at a major computer industry monthly publication for 7 years. Follow his data science musings at @AMULETAnalytics.","sameAs":["http:\/\/www.insidebigdata.com","https:\/\/twitter.com\/@AMULETAnalytics"],"url":"https:\/\/insidebigdata.com\/author\/dangutierrez\/"}]}},"jetpack_featured_media_url":"https:\/\/insidebigdata.com\/wp-content\/uploads\/2019\/06\/Data-Scientist-shutterstock_768047488.jpg","jetpack_shortlink":"https:\/\/wp.me\/p9eA3j-7Wq","jetpack-related-posts":[{"id":5924,"url":"https:\/\/insidebigdata.com\/2013\/11\/19\/machine-learning-branches\/","url_meta":{"origin":30530,"position":0},"title":"TECH TIP: Probabilistic Graphical Models","date":"November 19, 2013","format":false,"excerpt":"A recent announcement appearing in MIT News, \"Machine learning branches out,\" highlights new research in probabilistic graphical models. In a paper being presented in December at the annual conference of the Neural Information Processing Systems Foundation, MIT researchers describe a new technique that expands the class of data sets whose\u2026","rel":"","context":"In "Machine Learning"","img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":28903,"url":"https:\/\/insidebigdata.com\/2022\/04\/09\/research-highlights-deep-neural-networks-and-tabular-data-a-survey\/","url_meta":{"origin":30530,"position":1},"title":"Research Highlights: Deep Neural Networks and Tabular Data: A Survey","date":"April 9, 2022","format":false,"excerpt":"In this regular column, we take a look at highlights for important research topics of the day for big data, data science, machine learning, AI and deep learning. It\u2019s important to keep connected with the research arm of the field in order to see where we\u2019re headed. 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