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VERSION:2.0
PRODID:IEEE vTools.Events//EN
CALSCALE:GREGORIAN
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TZID:Canada/Eastern
BEGIN:DAYLIGHT
DTSTART:20210314T030000
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
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TZNAME:EDT
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BEGIN:STANDARD
DTSTART:20211107T010000
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TZOFFSETTO:-0500
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BEGIN:VEVENT
DTSTAMP:20210714T184053Z
UID:484DFCE0-892B-43F0-A2A8-78C27B50D836
DTSTART;TZID=Canada/Eastern:20211001T140000
DTEND;TZID=Canada/Eastern:20211001T153000
DESCRIPTION:This workshop will cover an example project on Bayes Classifier
 \, multiple random variables\, and estimation. We will learn the implement
 ation of multivariate Gaussian distribution\, classification and regressio
 n problems in Python. Later we will see that how to define parametric dist
 ribution in python and will further explore estimation concepts like maxim
 um likelihood ratio\, maximum posteriori classification\, loglikelihood an
 d logistic regression.\n\nSpeaker(s): Taha Sajjad\, \n\nVirtual: https://e
 vents.vtools.ieee.org/m/277453
LOCATION:Virtual: https://events.vtools.ieee.org/m/277453
ORGANIZER:ayda.naserialiabadi@ryerson.ca
SEQUENCE:0
SUMMARY:Applications of Probability in Python
URL;VALUE=URI:https://events.vtools.ieee.org/m/277453
X-ALT-DESC:Description: <br /><p>This&nbsp\;workshop&nbsp\;will cover an ex
 ample project on Bayes Classifier\, multiple random variables\, and estima
 tion. We will learn the implementation of multivariate Gaussian distributi
 on\, classification and regression problems in Python. Later we will see t
 hat how to define parametric distribution in python and will further explo
 re estimation concepts like maximum likelihood ratio\, maximum posteriori 
 classification\, loglikelihood and logistic regression.</p>
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END:VCALENDAR