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VERSION:2.0
PRODID:IEEE vTools.Events//EN
CALSCALE:GREGORIAN
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TZID:Asia/Kolkata
BEGIN:STANDARD
DTSTART:19451014T230000
TZOFFSETFROM:+0630
TZOFFSETTO:+0530
TZNAME:IST
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BEGIN:VEVENT
DTSTAMP:20240925T051025Z
UID:CE3519D7-A431-48F2-9FFA-AD7868FA03A2
DTSTART;TZID=Asia/Kolkata:20231204T090000
DTEND;TZID=Asia/Kolkata:20231209T170000
DESCRIPTION:Explainable artificial intelligence (XAI) is a method to unders
 tand “Why and How “outcomes\ncreated by machine learning algorithms ar
 e useful and applicable. Explainable AI describes an\nAI model\, its signi
 ficance and impact. It helps characterize model accuracy\, fairness\,\ntra
 nsparency and outcomes in AI-powered decision making. As most of the ML/DL
  algorithms\nare experimented for various application\, there is importanc
 e of justification of its outcomes.\nExplainable AI will add value to the 
 research carried out in this area.\n\nVirtual: https://events.vtools.ieee.
 org/m/377915
LOCATION:Virtual: https://events.vtools.ieee.org/m/377915
ORGANIZER:ieeesps2023@pccoepune.org
SEQUENCE:14
SUMMARY:2023 IEEE SPS Seasonal school on Explainable AI for Signal and Imag
 e Processing
URL;VALUE=URI:https://events.vtools.ieee.org/m/377915
X-ALT-DESC:Description: <br /><p>Explainable artificial intelligence (XAI) 
 is a method to understand &ldquo\;Why and How &ldquo\;outcomes<br />create
 d by machine learning algorithms are useful and applicable. Explainable AI
  describes an<br />AI model\, its significance and impact. It helps charac
 terize model accuracy\, fairness\,<br />transparency and outcomes in AI-po
 wered decision making. As most of the ML/DL algorithms<br />are experiment
 ed for various application\, there is importance of justification of its o
 utcomes.<br />Explainable AI will add value to the research carried out in
  this area.</p>
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END:VCALENDAR