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BEGIN:VEVENT
DTSTAMP:20260513T012944Z
DTSTART;VALUE=DATE-TIME:20190124T130000
DTEND;VALUE=DATE-TIME:20190124T140000
SUMMARY:Thomas Gaertner: Interactive Machine Learning
TZID:Europe/London
UID:20190124-8a17841a6850d8fc01686707807f2040@warwick.ac.uk
CREATED:20190121T130228Z
DESCRIPTION:Department Seminar Interactive Machine Learning Prof Thomas G
 aertner\, University of Nottingham Room CS101\, 13:00\, Thursday 24/01/1
 9 In this talk I'll give an overview of our contributions to what I call
  interactive machine learning. Often\, interaction in computer science i
 s interpreted as the interaction of humans with the computer but I inten
 d a broader meaning of the interaction of machine learning algorithms wi
 th the real world\, including but not restricted to humans. Interactions
  with humans span a broad range where they can be intentional and guided
  by the human or they can be guided by the computer such that the human 
 is oblivious of the fact that he is being guided. Another example of an 
 interaction with the real world is the use of machine learning algorithm
 s in cyclic discovery processes such as drug design. Important propertie
 s of interactive machine learning algorithms include efficiency\, effect
 iveness\, responsiveness\, and robustness. In the talk I will show how t
 hese can be achieved in a variety of interactive contexts.
LOCATION:
CATEGORIES:
LAST-MODIFIED:20190121T130228Z
ORGANIZER;CN=Sara Kalvala:
END:VEVENT
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