BEGIN:VCALENDAR VERSION:2.0 PRODID:-//IEEE Toronto Section - ECPv6.15.17//NONSGML v1.0//EN CALSCALE:GREGORIAN METHOD:PUBLISH X-ORIGINAL-URL:https://www.ieeetoronto.ca X-WR-CALDESC:Events for IEEE Toronto Section REFRESH-INTERVAL;VALUE=DURATION:PT1H X-Robots-Tag:noindex X-PUBLISHED-TTL:PT1H BEGIN:VTIMEZONE TZID:UTC BEGIN:STANDARD TZOFFSETFROM:+0000 TZOFFSETTO:+0000 TZNAME:UTC DTSTART:20200101T000000 END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTART;TZID=UTC:20211116T170000 DTEND;TZID=UTC:20211116T183000 DTSTAMP:20260418T064341 CREATED:20211030T112019Z LAST-MODIFIED:20211216T081306Z UID:10000478-1637082000-1637087400@www.ieeetoronto.ca SUMMARY:Generalizing from Training Data DESCRIPTION:Prerequisites: You do not need to have attended the earlier talks. If you know zero math and zero machine learning\, then this talk is for you. Jeff will do his best to explain fairly hard mathematics to you. If you know a bunch of math and/or a bunch machine learning\, then these talks are for you. Jeff tries to spin the ideas in new ways. Longer Abstract: There is some theory. If a machine is found that gives the correct answers on the randomly chosen training data without simply memorizing\, then we can prove that with high probability this same machine will also work well on never seen before instances drawn from the same distribution. The easy proof requires D>m\, where m is the number of bits needed to describe your learned machine and D is the number of train data items. A much harder proof (which we likely won’t cover) requires only D>VC\, where VC is VC-dimension (Vapnik–Chervonenkis) of your machine. The second requirement is easier to meet because VC