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DTSTART;TZID=US/Eastern:20210930T190000
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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): Prof. Pedro Domingos\, \n\nAgenda: \n7:00 pm - 
 Introductions\n\n7:10 pm - Professor Domingos presentation w/Q&A\n\n8:10 p
 m - Open Discussion\n\n8:30 pm - Close\n\nVirtual: https://events.vtools.i
 eee.org/m/281946
LOCATION:Virtual: https://events.vtools.ieee.org/m/281946
ORGANIZER:joel.i.goodman@ieee.org
SEQUENCE:6
SUMMARY:The Ultimate Software: Machine Learning and Intelligence
URL;VALUE=URI:https://events.vtools.ieee.org/m/281946
X-ALT-DESC:Description: <br /><p><span style="caret-color: #000000\; color:
  #000000\; font-family: Helvetica\; font-size: 14px\; font-style: normal\;
  font-variant-caps: normal\; font-weight: normal\; letter-spacing: normal\
 ; orphans: auto\; text-align: start\; text-indent: 0px\; text-transform: n
 one\; white-space: normal\; widows: auto\; word-spacing: 0px\; -webkit-tex
 t-size-adjust: auto\; -webkit-text-stroke-width: 0px\; text-decoration: no
 ne\; display: inline !important\; float: none\;">Machine learning is the a
 utomation of discovery. With it\, computers can program themselves instead
  of having to be programmed by us. Learning systems are widely used in sci
 ence\, business and government\, but are still shrouded in mystery. This t
 alk explains the five major paradigms in machine learning &ndash\; symboli
 c learning\, deep learning\, genetic algorithms\, Bayesian learning and re
 asoning by analogy &ndash\; and samples some of the major applications the
 y enable\, from automated biology to personalized recommendations. It conc
 ludes with a look at the future: what machine learning will bring us\, and
  the roadblocks\, </span><span style="caret-color: #000000\; color: #00000
 0\; font-family: Helvetica\; font-size: 14px\; font-style: normal\; font-v
 ariant-caps: normal\; font-weight: normal\; letter-spacing: normal\; orpha
 ns: auto\; text-align: start\; text-indent: 0px\; text-transform: none\; w
 hite-space: normal\; widows: auto\; word-spacing: 0px\; -webkit-text-size-
 adjust: auto\; -webkit-text-stroke-width: 0px\; text-decoration: none\; di
 splay: inline !important\; float: none\;">dangers\, and opportunities on t
 hat path.</span></p><br /><br />Agenda: <br /><p>7:00 pm - Introductions</
 p>\n<p>7:10 pm - Professor Domingos presentation w/Q&amp\;A</p>\n<p>8:10 p
 m - Open Discussion</p>\n<p>8:30 pm - Close</p>
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