BEGIN:VCALENDAR VERSION:2.0 PRODID:-//https://caida.ubc.ca//NONSGML iCalcreator 2.41.92// CALSCALE:GREGORIAN METHOD:PUBLISH UID:39383366-3138-4465-b936-326361316139 X-WR-RELCALID:efc09d74-9c93-479e-a94f-485231ddccde X-WR-TIMEZONE:America/Vancouver X-WR-CALNAME:Learning-Algorithms from Bayesian Principle - Emtiyaz Khan\, V isiting Professor\, TUAT\, Team Leader at RIKEN Center for AIP BEGIN:VTIMEZONE TZID:America/Vancouver TZUNTIL:20211107T090000Z BEGIN:STANDARD TZNAME:PST DTSTART:20191103T020000 TZOFFSETFROM:-0700 TZOFFSETTO:-0800 RDATE:20201101T020000 END:STANDARD BEGIN:DAYLIGHT TZNAME:PDT DTSTART:20190310T020000 TZOFFSETFROM:-0800 TZOFFSETTO:-0700 RDATE:20200308T020000 RDATE:20210314T020000 END:DAYLIGHT END:VTIMEZONE BEGIN:VEVENT UID:4177c876-97bb-4b04-af77-ed16c86b0cc8 DTSTAMP:20251124T203509Z CLASS:PUBLIC CREATED:20191211T233341Z DESCRIPTION:Abstract: In machine learning\, new learning algorithms are des igned by borrowing ideas from optimization and statistics followed by an e xtensive empirical efforts to make them practical. However\, there is a la ck of underlying principles to guide this process. I will present a stocha stic learning algorithm derived from Bayesian principle. Using this algori thm\, we can obtain a range of existing algorithms: from classical methods such as least-squares\, Newton's method\, and Kalman filter to new deep-l earning algorithms such as RMSprop and Adam. Surprisingly\, using the same principles\, new… DTSTART;TZID=America/Vancouver:20191216T160000 DTEND;TZID=America/Vancouver:20191216T170000 LAST-MODIFIED:20210611T165930Z LOCATION:ICCS - X836\, ICICS Computer Science\, 2366 Main Mall\, Vancouver\ , BC SUMMARY:SeminarLearning-Algorithms from Bayesian Principle - Emtiyaz Khan\, Visiting Professor\, TUAT\, Team Leader at RIKEN Center for AIP TRANSP:OPAQUE URL:https://caida.ubc.ca/event/learning-algorithms-bayesian-principle-emtiy az-khan-visiting-professor-tuat-team-leader-riken END:VEVENT END:VCALENDAR