In this blog post I will share with you the materials that I found most useful when I started doing some Bayesian Optimisation in my research. Bear in mind, I am a Chemist by training, so I approached this topic from a non-mathematical background (my eyes have to be persuaded to look at mathematical equations). Out of all the materials I have come across, I found these to be the most accessible. <\/p>\n\n\n\n\n\n\n\n
(1) Nando de Freitas’ lecture on Bayesian optimization and multi-armed bandits<\/a><\/p>\n\n\n\n This is a YouTube video of a University of British Columbia computer science lecture by Nando de Freitas on Bayesian Optimisation and multi-armed bandits. It manages to introduce the fundamentals of Bayesian Optimisation at a very gentle pace and explains each equation in detail (I like his annotations especially). It is very easy to follow and well worth the 1 h 20 mins – he is an excellent lecturer. <\/p>\n\n\n\n (2) GPyOpt IPython notebook examples <\/a> <\/p>\n\n\n\n Anything interactive is a thumbs up from me. GPyOpt<\/a> has a great manual, including several example notebooks, all well commented and easy to follow. I like their simple weather map example<\/a>, which is a very accessible problem and not field specific. Admittedly, I have referenced this analogy in several of my presentations which introduces Bayesian Optimisation. <\/p>\n\n\n\n (3) These two papers:<\/p>\n\n\n\n Frazier, P. I. (2018). A Tutorial on Bayesian Optimization, (Section 5), 1\u201322. <\/a> If you want to read some literature, try these two; the first is a tutorial and the second is a review. Both go from the basics to the more advance. <\/p>\n\n\n\n Finally, thanks to these materials, I am surely a bit wiser in Bayesian Optimisation but I definitely have still a lot to learn (if you have any more nice materials please point them my way)! Also a big thanks to fellow OPIG member, Lucian, for introducing many (most) of these materials to me (it also helps enormously if you have someone who does<\/em> know about Bayesian Optimisation sitting in the same office as you)!<\/p>\n\n\n\n <\/p>\n","protected":false},"excerpt":{"rendered":" In this blog post I will share with you the materials that I found most useful when I started doing some Bayesian Optimisation in my research. Bear in mind, I am a Chemist by training, so I approached this topic from a non-mathematical background (my eyes have to be persuaded to look at mathematical equations). Out of all the materials I […]<\/p>\n","protected":false},"author":38,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"nf_dc_page":"","wikipediapreview_detectlinks":true,"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"ngg_post_thumbnail":0,"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[29,189,258,227],"tags":[259],"ppma_author":[530],"class_list":["post-4961","post","type-post","status-publish","format-standard","hentry","category-code","category-machine-learning","category-optimization","category-python-code","tag-bayesian-optimization"],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"authors":[{"term_id":530,"user_id":38,"is_guest":0,"slug":"susan","display_name":"Susan Leung","avatar_url":"https:\/\/secure.gravatar.com\/avatar\/e1a367a2f8d7409be8aa6d2beff2d277525c90331ec34202854a9a5116b4eaa4?s=96&d=mm&r=g","0":null,"1":"","2":"","3":"","4":"","5":"","6":"","7":"","8":""}],"_links":{"self":[{"href":"https:\/\/www.blopig.com\/blog\/wp-json\/wp\/v2\/posts\/4961","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.blopig.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.blopig.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.blopig.com\/blog\/wp-json\/wp\/v2\/users\/38"}],"replies":[{"embeddable":true,"href":"https:\/\/www.blopig.com\/blog\/wp-json\/wp\/v2\/comments?post=4961"}],"version-history":[{"count":4,"href":"https:\/\/www.blopig.com\/blog\/wp-json\/wp\/v2\/posts\/4961\/revisions"}],"predecessor-version":[{"id":4974,"href":"https:\/\/www.blopig.com\/blog\/wp-json\/wp\/v2\/posts\/4961\/revisions\/4974"}],"wp:attachment":[{"href":"https:\/\/www.blopig.com\/blog\/wp-json\/wp\/v2\/media?parent=4961"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.blopig.com\/blog\/wp-json\/wp\/v2\/categories?post=4961"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.blopig.com\/blog\/wp-json\/wp\/v2\/tags?post=4961"},{"taxonomy":"author","embeddable":true,"href":"https:\/\/www.blopig.com\/blog\/wp-json\/wp\/v2\/ppma_author?post=4961"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
Shahriari, B., Swersky, K., Wang, Z., Adams, R. P., & de Freitas, N. (2016). Taking the Human Out of the Loop: A Review of Bayesian Optimization. <\/a>Proceedings of the IEEE<\/a><\/em>, <\/a>104<\/a><\/em>(1), 148\u2013175.<\/a><\/p>\n\n\n\n