BEGIN:VCALENDAR
VERSION:2.0
PRODID:IEEE vTools.Events//EN
CALSCALE:GREGORIAN
BEGIN:VTIMEZONE
TZID:US/Eastern
BEGIN:DAYLIGHT
DTSTART:20210314T030000
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3
TZNAME:EDT
END:DAYLIGHT
BEGIN:STANDARD
DTSTART:20211107T010000
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11
TZNAME:EST
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTAMP:20211003T194526Z
UID:126E5E5D-634B-4C4F-AE0C-38E97560CA68
DTSTART;TZID=US/Eastern:20210930T190000
DTEND;TZID=US/Eastern:20210930T203000
DESCRIPTION:Machine learning is the automation of discovery. With it\, comp
 uters can program themselves instead of having to be programmed by us. Lea
 rning systems are widely used in science\, business and government\, but a
 re still shrouded in mystery. This talk explains the five major paradigms 
 in machine learning – symbolic learning\, deep learning\, genetic algori
 thms\, Bayesian learning and reasoning by analogy – and samples some of 
 the major applications they enable\, from automated biology to personalize
 d recommendations. It concludes with a look at the future: what machine le
 arning will bring us\, and the roadblocks\, dangers\, and opportunities on
  that path.\n\nSpeaker(s): Dr. Pedro Domingos\, \n\nAgenda: \n7:00 pm - In
 troductions\n\n7:10 pm - Professor Domingos presentation w/Q&A\n\n8:10 pm 
 - Open Discussion\n\n8:30 pm - Close\n\nVirtual: https://events.vtools.iee
 e.org/m/282511
LOCATION:Virtual: https://events.vtools.ieee.org/m/282511
ORGANIZER:allen.jones@ieee.org
SEQUENCE:1
SUMMARY:THE ULTIMATE SOFTWARE: MACHINE LEARNING AND INTELLIGENCE
URL;VALUE=URI:https://events.vtools.ieee.org/m/282511
X-ALT-DESC:Description: <br /><p>Machine learning is the automation of disc
 overy. With it\, computers can program themselves instead of having to be 
 programmed by us. Learning systems are widely used in science\, business a
 nd government\, but are still shrouded in mystery. This talk explains the 
 five major paradigms in machine learning &ndash\; symbolic learning\, deep
  learning\, genetic algorithms\, Bayesian learning and reasoning by analog
 y &ndash\; and samples some of the major applications they enable\, from a
 utomated biology to personalized recommendations. It concludes with a look
  at the future: what machine learning will bring us\, and the roadblocks\,
 &nbsp\;dangers\, and opportunities on that path.</p><br /><br />Agenda: <b
 r /><p>7:00 pm - Introductions</p>\n<p>7:10 pm - Professor Domingos presen
 tation w/Q&amp\;A</p>\n<p>8:10 pm - Open Discussion</p>\n<p>8:30 pm - Clos
 e</p>
END:VEVENT
END:VCALENDAR