BEGIN:VCALENDAR PRODID;X-RICAL-TZSOURCE=TZINFO:-//Calagator//EN CALSCALE:GREGORIAN X-WR-CALNAME:Calagator METHOD:PUBLISH VERSION:2.0 BEGIN:VTIMEZONE TZID;X-RICAL-TZSOURCE=TZINFO:America/Los_Angeles BEGIN:DAYLIGHT DTSTART:20220313T020000 RDATE:20220313T020000 TZOFFSETFROM:-0800 TZOFFSETTO:-0700 TZNAME:PDT END:DAYLIGHT END:VTIMEZONE BEGIN:VEVENT CREATED;VALUE=DATE-TIME:20220623T193549Z DTEND;TZID=America/Los_Angeles;VALUE=DATE-TIME:20220629T190000 DTSTART;TZID=America/Los_Angeles;VALUE=DATE-TIME:20220629T170000 DTSTAMP;VALUE=DATE-TIME:20220623T193549Z LAST-MODIFIED;VALUE=DATE-TIME:20220623T193549Z UID:http://calagator.org/events/1250478375 DESCRIPTION:If there is anything that is universally true in machine lear ning\, it is that more data is always better. That’s why data scientist s always respond to the “How much data do you need?” question with “How much data can you get?” \;\n \;\nBut sometimes you don’t have mu ch data\, because: a) It’s genuinely hard to get\; or b) You haven’t be en gathering it for very long and it takes time. This is a much more com mon circumstance than you might think. And you are not alone if you're e xperiencing this issue. Luckily\, and not surprisingly\, the machine lea rning community has come up with numerous approaches for dealing with it . Come sit in on an exclusive conversation with Dr. Tim Oates\, professo r and practitioner\, who will share what appeals to intuition as to why the methods work and provide concrete examples of their application to s imple problems. \;\n \;\nWe will discuss transfer learning\, ope n source datasets and synthetic data\, few shot learning\, active learni ng\, and semi-supervised learning. By the end of the evening\, you shou ld understand what all of these methods are\, when they are applicable\, and what kinds of results you can expect from using them. \;\n \; \nDr. Tim Oates is Chief Data Scientist at Synaptiq and an Oros Family P rofessor of Computer Science and Technology in the Department of Compute r Science and Electrical Engineering at the University of Maryland Balti more County. He received a Ph.D. degree from the University of Massachu setts\, Amherst in 2001 with a focus on Artificial Intelligence and Mach ine Learning\, and spent a year as a postdoc in the MIT AI Lab. He is a n author or co-author of more than 150 peer reviewed papers in AI\, ML\, and data mining. Dr. Oates served as Chief Scientist for a big data st artup in the contact management space\, and has consulted in a wide vari ety of industries\, including healthcare\, construction\, amusement park s\, publishing\, and social media. \;\n \;\n\n\nTags: machine lear ning\, Artificial Intelligence\, labelling\, training\, models\n\nImport ed from: http://calagator.org/events/1250478375 URL:https://www.eventbrite.com/e/how-much-data-do-we-need-tickets-3711799 58247?aff=Calagator SUMMARY:How Much Data Do We Need LOCATION:Synaptiq office: 909 North Beech Street\, Portland OR 97227 US SEQUENCE:1 END:VEVENT END:VCALENDAR