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DESCRIPTION:Dear Colleagues\,\nDear IEEE Students\,\n\nYou are kindly invit
 ed to the full day AI/ML training entitled &quot;Building Intelligent Model&quot; wi
 th two trainer experts in this area.\n\nPlease register ASAP https://forms
 .gle/P7ffeU6DaYjdcoKDA to save your seat because the seats are limited.\n\
 nThis big event aims to unite students from various universities for a ful
 l day of training about AI and Machine Learning with real applications.\n\
 n[]\n\nThis training day can provide participants with a comprehensive und
 erstanding of the foundational concepts and practical techniques in AI and
  machine learning. ·\n\nAt the end of the event\, attendees should be abl
 e to:\n\no Prepare and process data effectively for AI &amp; machine learning 
 tasks.\n\no Understand and apply various AI &amp; machine learning algorithms.
 \n\no Implement AI &amp; machine learning models using Keras.\n\no Address key
  challenges in AI &amp; machine learning.\n\no Explore real-world applications
 \n\nRegards\,\nAbdallah Kassem\n(on Behalf of NDU IEEE SB\, IEEEE SIGHT-Le
 banon\, IEEE Computer Lebanon Chapter &amp; IEEE Lebanon JT chapter CAS/PE/PEL
 /IE)\n\nAgenda: \n8:00-8:45: Registration &amp; refreshment\n\n8:45-9:00: Open
 ing Ceremony\n\n9:00-12:00: Session 1: Data Preparation for Machine Learni
 ng\n\n1. Introduction to Data Preparation\n\n2. Understanding Different Ty
 pes of Data\n\n3. Data Collection and Exploration\n\n4. Data Cleaning and 
 Pre-processing\n\n5. Data Transformation Techniques\n\n6. Splitting the Da
 ta for Training and Testing\n\n12:00-13:00 Lunch Break\n\n13:00-16:00: Ses
 sion 2: Introduction to Machine Learning\n\n1. Introduction\n\n2. Supervis
 ed Learning (K-NN\, SVM\, ...)\n\n3. Unsupervised Learning (Kmeans\, ...)\
 n\n4. Practical Examples of Supervised and Unsupervised Learning\n\n5. Key
  Challenges in Machine Learning\n\n6. Real-world Applications of Supervise
 d and Unsupervised Learning\n\nBldg: Conference Room\, Notre Dame Universi
 ty\, Barsa\, Koura\, Lebanon\, Lebanon\, 0000
LOCATION:Bldg: Conference Room\, Notre Dame University\, Barsa\, Koura\, Le
 banon\, Lebanon\, 0000
ORGANIZER:akassem@ndu.edu.lb
SEQUENCE:33
SUMMARY:Building Intelligent Model
URL;VALUE=URI:https://events.vtools.ieee.org/m/450082
X-ALT-DESC:Description: &lt;br /&gt;&lt;p&gt;Dear Colleagues\,&lt;br&gt;Dear IEEE Students\,&lt;
 /p&gt;\n&lt;p&gt;You are kindly invited to the full day AI/ML training entitled &quot;Bu
 ilding Intelligent Model&quot; with two trainer experts in this area.&amp;nbsp\;&lt;/p
 &gt;\n&lt;p&gt;Please register ASAP &lt;a href=&quot;https://forms.gle/P7ffeU6DaYjdcoKDA&quot;&gt;h
 ttps://forms.gle/P7ffeU6DaYjdcoKDA&lt;/a&gt; to save your seat because the seats
  are limited.&lt;/p&gt;\n&lt;p class=&quot;MsoNormal&quot; style=&quot;text-align: left\; directio
 n: ltr\; unicode-bidi: embed\;&quot;&gt;&lt;span style=&quot;font-size: 13.5pt\; color: bl
 ack\;&quot;&gt;This big event aims to unite students from various universities for
  a full day of training about AI and Machine Learning with real applicatio
 ns.&lt;/span&gt;&lt;/p&gt;\n&lt;p class=&quot;MsoNormal&quot; style=&quot;text-align: left\; direction: 
 ltr\; unicode-bidi: embed\;&quot;&gt;&lt;span style=&quot;font-size: 13.5pt\; color: black
 \;&quot;&gt;&lt;img src=&quot;https://events.vtools.ieee.org/vtools_ui/media/display/b06c7
 049-648a-4ac6-b561-716ab4e72fda&quot; alt=&quot;&quot; width=&quot;986&quot; height=&quot;986&quot;&gt;&lt;/span&gt;&lt;/
 p&gt;\n&lt;p class=&quot;MsoNormal&quot; style=&quot;text-align: left\; direction: ltr\; unicod
 e-bidi: embed\;&quot;&gt;&lt;span style=&quot;font-size: 13.5pt\; color: black\;&quot;&gt;This tra
 ining day can provide participants with a comprehensive understanding of t
 he foundational concepts and practical techniques in AI and machine learni
 ng. &amp;middot\;&amp;nbsp\;&lt;/span&gt;&lt;/p&gt;\n&lt;p class=&quot;MsoNormal&quot; style=&quot;text-align: l
 eft\; direction: ltr\; unicode-bidi: embed\;&quot;&gt;&lt;span style=&quot;font-size: 13.5
 pt\; color: black\;&quot;&gt;At the end of the event\, attendees should be able to
 : &lt;/span&gt;&lt;/p&gt;\n&lt;p class=&quot;MsoListParagraphCxSpFirst&quot; style=&quot;mso-add-space: 
 auto\; text-align: left\; text-indent: -.25in\; mso-list: l0 level1 lfo1\;
  direction: ltr\; unicode-bidi: embed\; margin: 0in 0in .0001pt .5in\;&quot;&gt;&lt;!
 -- [if !supportLists]--&gt;&lt;span style=&quot;font-size: 13.5pt\; font-family: &#39;Cou
 rier New&#39;\; mso-fareast-font-family: &#39;Courier New&#39;\; color: black\;&quot;&gt;&lt;span
  style=&quot;mso-list: Ignore\;&quot;&gt;o&lt;span style=&quot;font: 7.0pt &#39;Times New Roman&#39;\;&quot;
 &gt;&amp;nbsp\;&amp;nbsp\; &lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;!--[endif]--&gt;&lt;span style=&quot;font-size:
  13.5pt\; color: black\;&quot;&gt;Prepare and process data effectively for AI &amp;amp
 \; machine learning tasks.&lt;/span&gt;&lt;/p&gt;\n&lt;p class=&quot;MsoListParagraphCxSpMiddl
 e&quot; style=&quot;mso-add-space: auto\; text-align: left\; text-indent: -.25in\; m
 so-list: l0 level1 lfo1\; direction: ltr\; unicode-bidi: embed\; margin: 0
 in 0in .0001pt .5in\;&quot;&gt;&lt;!-- [if !supportLists]--&gt;&lt;span style=&quot;font-size: 1
 3.5pt\; font-family: &#39;Courier New&#39;\; mso-fareast-font-family: &#39;Courier New
 &#39;\; color: black\;&quot;&gt;&lt;span style=&quot;mso-list: Ignore\;&quot;&gt;o&lt;span style=&quot;font: 7
 .0pt &#39;Times New Roman&#39;\;&quot;&gt;&amp;nbsp\;&amp;nbsp\; &lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;!--[endif]-
 -&gt;&lt;span style=&quot;font-size: 13.5pt\; color: black\;&quot;&gt;Understand and apply va
 rious AI &amp;amp\; machine learning algorithms. &lt;/span&gt;&lt;/p&gt;\n&lt;p class=&quot;MsoLis
 tParagraphCxSpMiddle&quot; style=&quot;mso-add-space: auto\; text-align: left\; text
 -indent: -.25in\; mso-list: l0 level1 lfo1\; direction: ltr\; unicode-bidi
 : embed\; margin: 0in 0in .0001pt .5in\;&quot;&gt;&lt;!-- [if !supportLists]--&gt;&lt;span 
 style=&quot;font-size: 13.5pt\; font-family: &#39;Courier New&#39;\; mso-fareast-font-f
 amily: &#39;Courier New&#39;\; color: black\;&quot;&gt;&lt;span style=&quot;mso-list: Ignore\;&quot;&gt;o&lt;
 span style=&quot;font: 7.0pt &#39;Times New Roman&#39;\;&quot;&gt;&amp;nbsp\;&amp;nbsp\; &lt;/span&gt;&lt;/span&gt;
 &lt;/span&gt;&lt;!--[endif]--&gt;&lt;span style=&quot;font-size: 13.5pt\; color: black\;&quot;&gt;Impl
 ement AI &amp;amp\; machine learning models using Keras. &lt;/span&gt;&lt;/p&gt;\n&lt;p class
 =&quot;MsoListParagraphCxSpMiddle&quot; style=&quot;mso-add-space: auto\; text-align: lef
 t\; text-indent: -.25in\; mso-list: l0 level1 lfo1\; direction: ltr\; unic
 ode-bidi: embed\; margin: 0in 0in .0001pt .5in\;&quot;&gt;&lt;!-- [if !supportLists]-
 -&gt;&lt;span style=&quot;font-size: 13.5pt\; font-family: &#39;Courier New&#39;\; mso-fareas
 t-font-family: &#39;Courier New&#39;\; color: black\;&quot;&gt;&lt;span style=&quot;mso-list: Igno
 re\;&quot;&gt;o&lt;span style=&quot;font: 7.0pt &#39;Times New Roman&#39;\;&quot;&gt;&amp;nbsp\;&amp;nbsp\; &lt;/span
 &gt;&lt;/span&gt;&lt;/span&gt;&lt;!--[endif]--&gt;&lt;span style=&quot;font-size: 13.5pt\; color: black
 \;&quot;&gt;Address key challenges in AI &amp;amp\; machine learning. &lt;/span&gt;&lt;/p&gt;\n&lt;p 
 class=&quot;MsoListParagraphCxSpLast&quot; style=&quot;mso-add-space: auto\; text-align: 
 left\; text-indent: -.25in\; mso-list: l0 level1 lfo1\; direction: ltr\; u
 nicode-bidi: embed\; margin: 0in 0in .0001pt .5in\;&quot;&gt;&lt;!-- [if !supportList
 s]--&gt;&lt;span style=&quot;font-size: 13.5pt\; font-family: &#39;Courier New&#39;\; mso-far
 east-font-family: &#39;Courier New&#39;\; color: black\;&quot;&gt;&lt;span style=&quot;mso-list: I
 gnore\;&quot;&gt;o&lt;span style=&quot;font: 7.0pt &#39;Times New Roman&#39;\;&quot;&gt;&amp;nbsp\;&amp;nbsp\; &lt;/s
 pan&gt;&lt;/span&gt;&lt;/span&gt;&lt;!--[endif]--&gt;&lt;span style=&quot;font-size: 13.5pt\; color: bl
 ack\;&quot;&gt;Explore real-world applications&lt;/span&gt;&lt;/p&gt;\n&lt;p&gt;Regards\,&lt;br&gt;Abdalla
 h Kassem&amp;nbsp\;&lt;br&gt;(on Behalf of NDU IEEE SB\, IEEEE SIGHT-Lebanon\, IEEE 
 Computer Lebanon Chapter &amp;amp\; IEEE Lebanon JT chapter CAS/PE/PEL/IE)&lt;/p&gt;
 &lt;br /&gt;&lt;br /&gt;Agenda: &lt;br /&gt;&lt;p class=&quot;MsoNormal&quot; style=&quot;text-align: left\; l
 ine-height: 150%\; direction: ltr\; unicode-bidi: embed\;&quot;&gt;&lt;span style=&quot;fo
 nt-size: 13.5pt\; line-height: 150%\; color: black\;&quot;&gt;8:00-8:45: Registrat
 ion &amp;amp\; refreshment&lt;/span&gt;&lt;/p&gt;\n&lt;p class=&quot;MsoNormal&quot; style=&quot;text-align:
  left\; line-height: 150%\; direction: ltr\; unicode-bidi: embed\;&quot;&gt;&lt;span 
 style=&quot;font-size: 13.5pt\; line-height: 150%\; color: black\;&quot;&gt;8:45-9:00: 
 Opening Ceremony&lt;/span&gt;&lt;/p&gt;\n&lt;p class=&quot;MsoNormal&quot; style=&quot;text-align: left\
 ; line-height: 150%\; direction: ltr\; unicode-bidi: embed\;&quot;&gt;&lt;span style=
 &quot;font-size: 13.5pt\; line-height: 150%\; color: black\;&quot;&gt;9:00-12:00: Sessi
 on 1: Data Preparation for Machine Learning &lt;/span&gt;&lt;/p&gt;\n&lt;p class=&quot;MsoNorm
 al&quot; style=&quot;text-align: left\; text-indent: .5in\; direction: ltr\; unicode
 -bidi: embed\;&quot;&gt;&lt;span style=&quot;font-size: 13.5pt\; color: black\;&quot;&gt;1. Introd
 uction to Data Preparation &lt;/span&gt;&lt;/p&gt;\n&lt;p class=&quot;MsoNormal&quot; style=&quot;text-a
 lign: left\; text-indent: .5in\; direction: ltr\; unicode-bidi: embed\;&quot;&gt;&lt;
 span style=&quot;font-size: 13.5pt\; color: black\;&quot;&gt;2. Understanding Different
  Types of Data &lt;/span&gt;&lt;/p&gt;\n&lt;p class=&quot;MsoNormal&quot; style=&quot;text-align: left\;
  text-indent: .5in\; direction: ltr\; unicode-bidi: embed\;&quot;&gt;&lt;span style=&quot;
 font-size: 13.5pt\; color: black\;&quot;&gt;3. Data Collection and Exploration &lt;/s
 pan&gt;&lt;/p&gt;\n&lt;p class=&quot;MsoNormal&quot; style=&quot;text-align: left\; text-indent: .5in
 \; direction: ltr\; unicode-bidi: embed\;&quot;&gt;&lt;span style=&quot;font-size: 13.5pt\
 ; color: black\;&quot;&gt;4. Data Cleaning and Pre-processing &lt;/span&gt;&lt;/p&gt;\n&lt;p clas
 s=&quot;MsoNormal&quot; style=&quot;text-align: left\; text-indent: .5in\; direction: ltr
 \; unicode-bidi: embed\;&quot;&gt;&lt;span style=&quot;font-size: 13.5pt\; color: black\;&quot;
 &gt;5. Data Transformation Techniques &lt;/span&gt;&lt;/p&gt;\n&lt;p class=&quot;MsoNormal&quot; style
 =&quot;text-align: left\; text-indent: .5in\; line-height: 150%\; direction: lt
 r\; unicode-bidi: embed\;&quot;&gt;&lt;span style=&quot;font-size: 13.5pt\; line-height: 1
 50%\; color: black\;&quot;&gt;6. Splitting the Data for Training and Testing &lt;/spa
 n&gt;&lt;/p&gt;\n&lt;p class=&quot;MsoNormal&quot; style=&quot;text-align: left\; line-height: 150%\;
  direction: ltr\; unicode-bidi: embed\;&quot;&gt;&lt;span style=&quot;font-size: 13.5pt\; 
 line-height: 150%\; color: black\;&quot;&gt;12:00-13:00 Lunch Break&lt;/span&gt;&lt;/p&gt;\n&lt;p
  class=&quot;MsoNormal&quot; style=&quot;text-align: left\; line-height: 150%\; direction
 : ltr\; unicode-bidi: embed\;&quot;&gt;&lt;span style=&quot;font-size: 13.5pt\; line-heigh
 t: 150%\; color: black\;&quot;&gt;13:00-16:00: Session 2: Introduction to Machine 
 Learning &lt;/span&gt;&lt;/p&gt;\n&lt;p class=&quot;MsoNormal&quot; style=&quot;text-align: left\; text-
 indent: .5in\; direction: ltr\; unicode-bidi: embed\;&quot;&gt;&lt;span style=&quot;font-s
 ize: 13.5pt\; color: black\;&quot;&gt;1. Introduction &lt;/span&gt;&lt;/p&gt;\n&lt;p class=&quot;MsoNo
 rmal&quot; style=&quot;margin-left: .5in\; text-align: left\; direction: ltr\; unico
 de-bidi: embed\;&quot;&gt;&lt;span style=&quot;font-size: 13.5pt\; color: black\;&quot;&gt;2. Supe
 rvised Learning (K-NN\, SVM\, ...) &lt;/span&gt;&lt;/p&gt;\n&lt;p class=&quot;MsoNormal&quot; style
 =&quot;margin-left: .5in\; text-align: left\; direction: ltr\; unicode-bidi: em
 bed\;&quot;&gt;&lt;span style=&quot;font-size: 13.5pt\; color: black\;&quot;&gt;3. Unsupervised Le
 arning (Kmeans\, ...) &lt;/span&gt;&lt;/p&gt;\n&lt;p class=&quot;MsoNormal&quot; style=&quot;margin-left
 : .5in\; text-align: left\; direction: ltr\; unicode-bidi: embed\;&quot;&gt;&lt;span 
 style=&quot;font-size: 13.5pt\; color: black\;&quot;&gt;4. Practical Examples of Superv
 ised and Unsupervised Learning &lt;/span&gt;&lt;/p&gt;\n&lt;p class=&quot;MsoNormal&quot; style=&quot;ma
 rgin-left: .5in\; text-align: left\; direction: ltr\; unicode-bidi: embed\
 ;&quot;&gt;&lt;span style=&quot;font-size: 13.5pt\; color: black\;&quot;&gt;5. Key Challenges in M
 achine Learning &lt;/span&gt;&lt;/p&gt;\n&lt;p class=&quot;MsoNormal&quot; style=&quot;margin-left: .5in
 \; text-align: left\; direction: ltr\; unicode-bidi: embed\;&quot;&gt;&lt;span style=
 &quot;font-size: 13.5pt\; color: black\;&quot;&gt;6. Real-world Applications of Supervi
 sed and Unsupervised Learning&lt;/span&gt;&lt;/p&gt;
END:VEVENT
END:VCALENDAR