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PRODID:IEEE vTools.Events//EN
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BEGIN:DAYLIGHT
DTSTART:20190310T030000
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DTSTART:20191103T010000
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BEGIN:VEVENT
DTSTAMP:20190615T151511Z
UID:D8AA3229-EB25-4189-A013-6895469DDD87
DTSTART;TZID=US/Eastern:20190612T083000
DTEND;TZID=US/Eastern:20190614T180000
DESCRIPTION:Artificial intelligence is profoundly changing our lives. Fuele
 d by recent advances in deep learning\, fields such as computer vision\, s
 peech recognition\, and pattern recognition are being transformed. Advance
 s in technology are redefining applications in human computer interaction\
 , advanced manufacturing\, social networking\, autonomous systems\, securi
 ty\, and entertainment. RIT’s practical 3-day workshop is designed to br
 ing you up to speed on the hottest topics in machine and deep learning. Yo
 u will build AI models during hands-on exercises and learn how to apply co
 ncepts and tools to challenges and opportunities within your organization.
 \n\nFor more information and registration\, please go to: http://www.rit.e
 du/kgcoe/cqas/machinelearning\n\nCo-sponsored by: RIT\n\nSpeaker(s): Majid
  Rabbani\, Ray Ptucha\n\nAgenda: \n- Day 1: 9:00 am – 5:00 pm\n- Introdu
 ction to Machine Learning: regression\, classification boosting\, SVM\, ne
 ural networks and dimensionality reduction\n- Machine Learning Methods &amp; h
 ands-on exercises: introduction to Python programming\, hands-on Python an
 d machine learning\n\n- Day 2: 9:00 am – 5:00 pm\n- Deep Learning Part I
 : convolutional neural networks\, regional and pixel-level convolutions\n-
  Deep Learning CNN &amp; hands-on exercises\n\n- Day 3: 9:00 am – 5:00 pm\n-
  Deep Learning Part II: recurrent neural networks\, language and vision\, 
 AI and its impact on the future\n- Practical uses &amp; hands-on exercises: re
 gional CNN’s and RNN’s\n\nRochester Institute of Technology\, Rocheste
 r\, New York\, United States\, 14623-5603
LOCATION:Rochester Institute of Technology\, Rochester\, New York\, United 
 States\, 14623-5603
ORGANIZER:rwpeec@rit.edu
SEQUENCE:1
SUMMARY:Machine Intelligence &amp; Deep Learning
URL;VALUE=URI:https://events.vtools.ieee.org/m/199434
X-ALT-DESC:Description: &lt;br /&gt;&lt;p&gt;Artificial intelligence is profoundly chan
 ging our lives.&amp;nbsp\; Fueled by recent advances in deep learning\, fields
  such as computer vision\, speech recognition\, and pattern recognition ar
 e being transformed.&amp;nbsp\; Advances in technology are redefining applicat
 ions in human computer interaction\, advanced manufacturing\, social netwo
 rking\, autonomous systems\, security\, and entertainment.&amp;nbsp\; RIT&amp;rsqu
 o\;s practical 3-day workshop is designed to bring you up to speed on the 
 hottest topics in machine and deep learning.&amp;nbsp\; You will build AI mode
 ls during hands-on exercises and learn how to apply concepts and tools to 
 challenges and opportunities within your organization.&lt;/p&gt;\n&lt;p&gt;For more in
 formation and registration\, please go to:&amp;nbsp\;http://www.rit.edu/kgcoe/
 cqas/machinelearning&lt;/p&gt;&lt;br /&gt;&lt;br /&gt;Agenda: &lt;br /&gt;&lt;ul&gt;\n&lt;li&gt;Day 1: 9:00 am
  &amp;ndash\; 5:00 pm\n&lt;ul&gt;\n&lt;li&gt;Introduction to Machine Learning: regression\
 , classification boosting\, SVM\, neural networks and dimensionality reduc
 tion&lt;/li&gt;\n&lt;li&gt;Machine Learning Methods &amp;amp\; hands-on exercises: introdu
 ction to Python programming\, hands-on Python and machine learning&lt;/li&gt;\n&lt;
 /ul&gt;\n&lt;/li&gt;\n&lt;li&gt;Day 2: 9:00 am &amp;ndash\; 5:00 pm\n&lt;ul&gt;\n&lt;li&gt;Deep Learning 
 Part I: convolutional neural networks\, regional and pixel-level convoluti
 ons&lt;/li&gt;\n&lt;li&gt;Deep Learning CNN &amp;amp\; hands-on exercises&lt;/li&gt;\n&lt;/ul&gt;\n&lt;/l
 i&gt;\n&lt;li&gt;Day 3:&amp;nbsp\;9:00 am &amp;ndash\; 5:00 pm\n&lt;ul&gt;\n&lt;li&gt;Deep Learning Par
 t II: recurrent neural networks\, language and vision\, AI and its impact 
 on the future&lt;/li&gt;\n&lt;li&gt;Practical uses &amp;amp\; hands-on exercises: regional
  CNN&amp;rsquo\;s and RNN&amp;rsquo\;s&lt;/li&gt;\n&lt;/ul&gt;\n&lt;/li&gt;\n&lt;/ul&gt;
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