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PRODID:IEEE vTools.Events//EN
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TZID:Canada/Eastern
BEGIN:DAYLIGHT
DTSTART:20210314T030000
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DTSTART:20211107T010000
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DTSTAMP:20210714T181715Z
UID:BC1BE635-1DE9-4D53-B0E5-CE9D85EAA6F9
DTSTART;TZID=Canada/Eastern:20210927T140000
DTEND;TZID=Canada/Eastern:20210927T153000
DESCRIPTION:In this workshop\, we first provide a brief review of probabili
 ty theory making sure that attendees understand probability models and app
 lications. Later in this workshop\, we will discuss basic probability mode
 ls and their implementation in python\, how to deal with various aspects o
 f conditional probability like total probability theorem\, conditional ind
 ependence\, Bayes Rule\, etc. Then\, we will discuss the implementation of
  discrete random variables as well as continuous random variable like Bern
 oulli variables\, geometric variables\, uniform\, exponential and gaussian
  distribution. Afterwards\, fundamental law of large numbers related progr
 amming concepts will be covered along with sample mean and variance of fam
 ous probability distributions.\n\nSpeaker(s): Taha Sajjad\, \n\nVirtual: h
 ttps://events.vtools.ieee.org/m/277449
LOCATION:Virtual: https://events.vtools.ieee.org/m/277449
ORGANIZER:ayda.naserialiabadi@ryerson.ca
SEQUENCE:1
SUMMARY:Fundamentals of Probability in Python
URL;VALUE=URI:https://events.vtools.ieee.org/m/277449
X-ALT-DESC:Description: <br /><p>In this workshop\, we first provide a brie
 f review of probability theory making sure that attendees understand proba
 bility models and applications. Later in this workshop\, we will discuss b
 asic probability models and their implementation in python\, how to deal w
 ith various aspects of conditional probability like total probability theo
 rem\, conditional independence\, Bayes Rule\, etc. Then\, we will discuss 
 the implementation of discrete random variables as well as continuous rand
 om variable like Bernoulli variables\, geometric variables\, uniform\, exp
 onential and gaussian distribution. Afterwards\, fundamental law of large 
 numbers related programming concepts will be covered along with sample mea
 n and variance of famous probability distributions.</p>
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