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PRODID:IEEE vTools.Events//EN
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DTSTART:20240310T030000
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DTSTART:20231105T010000
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BEGIN:VEVENT
DTSTAMP:20231112T223419Z
UID:E70A9E78-9DAB-446A-993C-D880F7E6C9BB
DTSTART;TZID=America/Los_Angeles:20231109T103000
DTEND;TZID=America/Los_Angeles:20231109T113000
DESCRIPTION:Engineers and data scientists work with large amounts of data i
 n a variety of formats such as sensor\, image\, video\, telemetry\, databa
 ses\, and more. They use machine learning to find patterns in data and to 
 build models that predict future outcomes based on historical data.\nIn th
 is session\, we explore the fundamentals of machine learning using MATLAB.
  We introduce machine learning techniques available in MATLAB to quickly e
 xplore your data\, evaluate machine learning algorithms\, compare the resu
 lts\, and apply the best technique to your problem.\nHighlights include:\n
 • Training\, evaluating\, and comparing a range of machine learning mode
 ls\n• Using refinement and reduction techniques to create models that be
 st capture the predictive power of your data\n• Running predictive model
 s in parallel using multiple processors to expedite your results\n• Depl
 oying your models to production in a variety of formats\n\nSpeaker(s): \, 
 Dr. Neha Sardesai\n\nVirtual: https://events.vtools.ieee.org/m/379977
LOCATION:Virtual: https://events.vtools.ieee.org/m/379977
ORGANIZER:ava.hedayatipour@csulb.edu
SEQUENCE:12
SUMMARY:Machine Learning with MATLAB
URL;VALUE=URI:https://events.vtools.ieee.org/m/379977
X-ALT-DESC:Description: <br /><p>Engineers and data scientists work with la
 rge amounts of data in a variety of formats such as sensor\, image\, video
 \, telemetry\, databases\, and more. They use machine learning to find pat
 terns in data and to build models that predict future outcomes based on hi
 storical data.<br />In this session\, we explore the fundamentals of machi
 ne learning using MATLAB. We introduce machine learning techniques availab
 le in MATLAB to quickly explore your data\, evaluate machine learning algo
 rithms\, compare the results\, and apply the best technique to your proble
 m.<br />Highlights include:<br />&bull\; Training\, evaluating\, and compa
 ring a range of machine learning models<br />&bull\; Using refinement and 
 reduction techniques to create models that best capture the predictive pow
 er of your data<br />&bull\; Running predictive models in parallel using m
 ultiple processors to expedite your results<br />&bull\; Deploying your mo
 dels to production in a variety of formats</p>
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