BEGIN:VCALENDAR
VERSION:2.0
PRODID:IEEE vTools.Events//EN
CALSCALE:GREGORIAN
BEGIN:VTIMEZONE
TZID:Asia/Kolkata
BEGIN:STANDARD
DTSTART:19451014T230000
TZOFFSETFROM:+0630
TZOFFSETTO:+0530
TZNAME:IST
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTAMP:20230412T192501Z
UID:270CF4D8-A5D2-4ECC-B2B1-7F2CC4D77C01
DTSTART;TZID=Asia/Kolkata:20230401T130000
DTEND;TZID=Asia/Kolkata:20230401T170000
DESCRIPTION:A Machine learning based treasure hunt where participants use t
 heir skills of ML and data science to solve one problem after the other. T
 he solution to the problem leads to the next. Fun for all participants - a
 mateurs and the experienced.\n\nAgenda: \nThis was a part of the Technical
  fest at BITS Pilani\, APOGEE 2023. This event aimed at encouraging studen
 ts from universities across India to take part in a machine learning conte
 st where the winner is decided on the merits of critical thinking and anal
 ytical abilities\, instead of just training the most accurate model. In th
 is contest\, traditional barriers to machine learning for amateurs such as
  lack of large scale parallel processing compute are not present.\n\nVirtu
 al: https://events.vtools.ieee.org/m/357154
LOCATION:Virtual: https://events.vtools.ieee.org/m/357154
ORGANIZER:ieee.sb@pilani.bits-pilani.ac.in
SEQUENCE:0
SUMMARY:ML Treasure Hunt
URL;VALUE=URI:https://events.vtools.ieee.org/m/357154
X-ALT-DESC:Description: <br /><p>A Machine learning based treasure hunt whe
 re participants use their skills of ML and data science to solve one probl
 em after the other. The solution to the problem leads to the next. Fun for
  all participants - amateurs and the experienced.</p><br /><br />Agenda: <
 br /><p>This was a part of the Technical fest at BITS Pilani\, APOGEE 2023
 . This event aimed at encouraging students from universities across India 
 to take part in a machine learning contest where the winner is decided on 
 the merits of critical thinking and analytical abilities\, instead of just
  training the most accurate model. In this contest\, traditional barriers 
 to machine learning for amateurs such as lack of large scale parallel proc
 essing compute are not present.</p>
END:VEVENT
END:VCALENDAR