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DTSTART:20380119T061407
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DTSTART:19470313T235308
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DTSTAMP:20200217T114052Z
UID:0870B173-F35F-47AC-ADB2-0A50822AEC6C
DTSTART;TZID=Asia/Riyadh:20190430T080000
DTEND;TZID=Asia/Riyadh:20190501T140000
DESCRIPTION:The main goal of this workshop is to provide a training course 
 for remote sensing researchers and practitioners in Saudi Arabia\, from bo
 th government and private sectors\, on how to best use deep learning Tenso
 rflow API. The course will be taught using online material and computation
 al resources and by experts in machine learning. The participants need to 
 bring their own laptop to access the learning material.\n\nThe workshop wi
 ll answer questions like:\n\n- How does machine learning differ from tradi
 tional programming?\n- What is loss\, and how do I measure it?\n- How does
  gradient descent work?\n- How do I determine whether my model is effectiv
 e?\n- How do I represent my data so that a program can learn from it?\n- H
 ow do I build a deep neural network?\n\nParticipants will also apply the l
 earned techniques on remote sensing problems. Upon completion\, Participan
 ts will be able to start solving problems on their own with deep learning.
 \n\nUseful background for taking the course:\n\nYou need to bring a laptop
  to do the practical exercises in this workshop.\n\nMastery of intro-level
  algebra. You should be comfortable with variables and coefficients\, line
 ar equations\, graphs of functions\, and histograms. (Familiarity with mor
 e advanced math concepts such as logarithms and derivatives is helpful\, b
 ut not required.)\n\nProficiency in programming basics\, and some experien
 ce coding in Python. Programming exercises in Machine Learning Crash Cours
 e are coded in Python using TensorFlow. No prior experience with TensorFlo
 w is required\, but you should feel comfortable reading and writing Python
  code that contains basic programming constructs\, such as function defini
 tions/invocations\, lists and dicts\, loops\, and conditional expressions.
 \n\nCo-sponsored by: King Saud University\n\nSpeaker(s): Haikel Hichri\, N
 assim Ammour\, Haidar Almubarak\, M. Alrahhal\, Yacoub Bazi\n\nAgenda: \nD
 ay 1 (Tuesday\, 30 April 2019)\n\nTime\n\nActivity\n\n8:00 – 8:30\n\nReg
 istration and breakfast\n\n8:30-9:00\n\nIntroduction to GRSS Saudi Arabia 
 chapter\n\nBy Dr. Haikel Hichri\n\n9:00-9:30\n\nIntroduction to remote sen
 sing\n\nBy Dr. Haikel Hichri\n\n9:30-10:00\n\nMachine learning concepts\, 
 Introduction to ML\, Framing.\n\nBy Dr. Haidar Almubarak\n\n10:00-10:15\n\
 nCoffee break\n\n10:15-11:45\n\nDescending into ML\, Reducing Loss.\n\nBy 
 Dr. Haidar Almubarak\n\n11:45-12:15\n\nSalat/Namaz break\n\n12:15-13:30\n\
 nFirst Steps with TF\, Generalization\, Training and Test Sets.\n\nBy Dr. 
 Nassim Ammour\n\nDay 2 (Wednesday\, 1 May 2019)\n\nTime\n\nActivity\n\n8:0
 0 – 8:30\n\nBreakfast\n\n8:30-10:00\n\nLogistic Regression\, Classificat
 ion.\n\nBy Dr. Alrahhal\n\n10:00-10:15\n\nCoffee break\n\n10:15-11:45\n\nN
 eural Networks\, Training Neural Nets.\n\nBy Dr. Yacoub bazi\n\n11:45-12:1
 5\n\nSalat/Namaz break\n\n12:15-13:30\n\nMulti-Class Neural Nets using rem
 ote sensing example.\n\nBy Dr. Haikel Hichri\n\nRoom: Auditorium G56(male 
 side)/G21(female side)\, Bldg: 31 (male section)/ 6 for female section\, P
 o Box 51178\, Computer Engineering\, College of computer and info sciences
 \, Riyadh\, Ar Riyad\, Saudi Arabia\, 11543
LOCATION:Room: Auditorium G56(male side)/G21(female side)\, Bldg: 31 (male 
 section)/ 6 for female section\, Po Box 51178\, Computer Engineering\, Col
 lege of computer and info sciences\, Riyadh\, Ar Riyad\, Saudi Arabia\, 11
 543
ORGANIZER:hhichri@ksu.edu.sa
SEQUENCE:31
SUMMARY:Machine learning for remote sensing crash course
URL;VALUE=URI:https://events.vtools.ieee.org/m/196587
X-ALT-DESC:Description: &lt;br /&gt;&lt;p&gt;The main goal of this workshop is to provi
 de a training course for remote sensing researchers and practitioners in S
 audi Arabia\, from both government and private sectors\, on how to best us
 e deep learning Tensorflow API. The course will be taught using online mat
 erial and computational resources and&amp;nbsp\;by experts in machine learning
 . The participants need to bring their own laptop to access the learning m
 aterial.&amp;nbsp\;&lt;/p&gt;\n&lt;p&gt;The workshop will answer questions like:&lt;/p&gt;\n&lt;ul&gt;
 \n&lt;li&gt;How does machine learning differ from traditional programming?&lt;/li&gt;\
 n&lt;li&gt;What is loss\, and how do I measure it?&lt;/li&gt;\n&lt;li&gt;How does gradient d
 escent work?&lt;/li&gt;\n&lt;li&gt;How do I determine whether my model is effective?&lt;/
 li&gt;\n&lt;li&gt;How do I represent my data so that a program can learn from it?&lt;/
 li&gt;\n&lt;li&gt;How do I build a deep neural network?&lt;/li&gt;\n&lt;/ul&gt;\n&lt;p&gt;Participant
 s will also apply the learned techniques on remote sensing problems. Upon 
 completion\, Participants will be able to start solving problems on their 
 own with deep learning.&lt;/p&gt;\n&lt;p&gt;Useful background for taking the course:&lt;/
 p&gt;\n&lt;p&gt;You need to bring a laptop to do the practical exercises in this wo
 rkshop.&lt;/p&gt;\n&lt;p&gt;Mastery of intro-level algebra. You should be comfortable 
 with variables and coefficients\, linear equations\, graphs of functions\,
  and histograms. (Familiarity with more advanced math concepts such as log
 arithms and derivatives is helpful\, but not required.)&lt;/p&gt;\n&lt;p&gt;Proficienc
 y in programming basics\, and some experience coding in Python. Programmin
 g exercises in Machine Learning Crash Course are coded in Python using Ten
 sorFlow. No prior experience with TensorFlow is required\, but you should 
 feel comfortable reading and writing Python code that contains basic progr
 amming constructs\, such as function definitions/invocations\, lists and d
 icts\, loops\, and conditional expressions.&lt;/p&gt;&lt;br /&gt;&lt;br /&gt;Agenda: &lt;br /&gt;&lt;
 p&gt;Day 1 (Tuesday\, 30 April 2019)&lt;/p&gt;\n&lt;table&gt;\n&lt;tbody&gt;\n&lt;tr&gt;\n&lt;td width=&quot;
 130&quot;&gt;\n&lt;p&gt;Time&lt;/p&gt;\n&lt;/td&gt;\n&lt;td width=&quot;460&quot;&gt;\n&lt;p&gt;Activity&lt;/p&gt;\n&lt;/td&gt;\n&lt;/tr&gt;
 \n&lt;tr&gt;\n&lt;td width=&quot;130&quot;&gt;\n&lt;p&gt;8:00 &amp;ndash\; 8:30&lt;/p&gt;\n&lt;/td&gt;\n&lt;td width=&quot;460
 &quot;&gt;\n&lt;p&gt;Registration and breakfast&lt;/p&gt;\n&lt;/td&gt;\n&lt;/tr&gt;\n&lt;tr&gt;\n&lt;td width=&quot;130&quot;
 &gt;\n&lt;p&gt;8:30-9:00&lt;/p&gt;\n&lt;/td&gt;\n&lt;td width=&quot;460&quot;&gt;\n&lt;p&gt;Introduction to GRSS Saud
 i Arabia chapter&lt;/p&gt;\n&lt;p&gt;By Dr. Haikel Hichri&lt;/p&gt;\n&lt;/td&gt;\n&lt;/tr&gt;\n&lt;tr&gt;\n&lt;td
  width=&quot;130&quot;&gt;\n&lt;p&gt;9:00-9:30&lt;/p&gt;\n&lt;/td&gt;\n&lt;td width=&quot;460&quot;&gt;\n&lt;p&gt;Introduction 
 to remote sensing&lt;/p&gt;\n&lt;p&gt;By Dr. Haikel Hichri&lt;/p&gt;\n&lt;/td&gt;\n&lt;/tr&gt;\n&lt;tr&gt;\n&lt;t
 d width=&quot;130&quot;&gt;\n&lt;p&gt;9:30-10:00&lt;/p&gt;\n&lt;/td&gt;\n&lt;td width=&quot;460&quot;&gt;\n&lt;p&gt;Machine lea
 rning concepts\, Introduction to ML\, Framing.&lt;/p&gt;\n&lt;p&gt;By Dr. Haidar Almub
 arak&lt;/p&gt;\n&lt;/td&gt;\n&lt;/tr&gt;\n&lt;tr&gt;\n&lt;td width=&quot;130&quot;&gt;\n&lt;p&gt;10:00-10:15&lt;/p&gt;\n&lt;/td&gt;\
 n&lt;td width=&quot;460&quot;&gt;\n&lt;p&gt;Coffee break&lt;/p&gt;\n&lt;/td&gt;\n&lt;/tr&gt;\n&lt;tr&gt;\n&lt;td width=&quot;130
 &quot;&gt;\n&lt;p&gt;10:15-11:45&lt;/p&gt;\n&lt;/td&gt;\n&lt;td width=&quot;460&quot;&gt;\n&lt;p&gt;Descending into ML\, R
 educing Loss.&lt;/p&gt;\n&lt;p&gt;By Dr. Haidar Almubarak&lt;/p&gt;\n&lt;/td&gt;\n&lt;/tr&gt;\n&lt;tr&gt;\n&lt;td
  width=&quot;130&quot;&gt;\n&lt;p&gt;11:45-12:15&lt;/p&gt;\n&lt;/td&gt;\n&lt;td width=&quot;460&quot;&gt;\n&lt;p&gt;Salat/Namaz
  break&lt;/p&gt;\n&lt;/td&gt;\n&lt;/tr&gt;\n&lt;tr&gt;\n&lt;td width=&quot;130&quot;&gt;\n&lt;p&gt;12:15-13:30&lt;/p&gt;\n&lt;/td
 &gt;\n&lt;td width=&quot;460&quot;&gt;\n&lt;p&gt;First Steps with TF\, Generalization\, Training an
 d Test Sets.&lt;/p&gt;\n&lt;p&gt;By Dr. Nassim Ammour&lt;/p&gt;\n&lt;/td&gt;\n&lt;/tr&gt;\n&lt;/tbody&gt;\n&lt;/t
 able&gt;\n&lt;p&gt;&amp;nbsp\;&lt;/p&gt;\n&lt;p&gt;Day 2 (Wednesday\, 1 May 2019)&lt;/p&gt;\n&lt;table&gt;\n&lt;tb
 ody&gt;\n&lt;tr&gt;\n&lt;td width=&quot;130&quot;&gt;\n&lt;p&gt;Time&lt;/p&gt;\n&lt;/td&gt;\n&lt;td width=&quot;460&quot;&gt;\n&lt;p&gt;Act
 ivity&lt;/p&gt;\n&lt;/td&gt;\n&lt;/tr&gt;\n&lt;tr&gt;\n&lt;td width=&quot;130&quot;&gt;\n&lt;p&gt;8:00 &amp;ndash\; 8:30&lt;/p&gt;
 \n&lt;/td&gt;\n&lt;td width=&quot;460&quot;&gt;\n&lt;p&gt;Breakfast&lt;/p&gt;\n&lt;/td&gt;\n&lt;/tr&gt;\n&lt;tr&gt;\n&lt;td width
 =&quot;130&quot;&gt;\n&lt;p&gt;8:30-10:00&lt;/p&gt;\n&lt;/td&gt;\n&lt;td width=&quot;460&quot;&gt;\n&lt;p&gt;Logistic Regressio
 n\, Classification.&lt;/p&gt;\n&lt;p&gt;By Dr. Alrahhal&lt;/p&gt;\n&lt;/td&gt;\n&lt;/tr&gt;\n&lt;tr&gt;\n&lt;td w
 idth=&quot;130&quot;&gt;\n&lt;p&gt;10:00-10:15&lt;/p&gt;\n&lt;/td&gt;\n&lt;td width=&quot;460&quot;&gt;\n&lt;p&gt;Coffee break&lt;
 /p&gt;\n&lt;/td&gt;\n&lt;/tr&gt;\n&lt;tr&gt;\n&lt;td width=&quot;130&quot;&gt;\n&lt;p&gt;10:15-11:45&lt;/p&gt;\n&lt;/td&gt;\n&lt;td 
 width=&quot;460&quot;&gt;\n&lt;p&gt;Neural Networks\, Training Neural Nets.&lt;/p&gt;\n&lt;p&gt;By Dr. Ya
 coub bazi&lt;/p&gt;\n&lt;/td&gt;\n&lt;/tr&gt;\n&lt;tr&gt;\n&lt;td width=&quot;130&quot;&gt;\n&lt;p&gt;11:45-12:15&lt;/p&gt;\n&lt;
 /td&gt;\n&lt;td width=&quot;460&quot;&gt;\n&lt;p&gt;Salat/Namaz break&lt;/p&gt;\n&lt;/td&gt;\n&lt;/tr&gt;\n&lt;tr&gt;\n&lt;td 
 width=&quot;130&quot;&gt;\n&lt;p&gt;12:15-13:30&lt;/p&gt;\n&lt;/td&gt;\n&lt;td width=&quot;460&quot;&gt;\n&lt;p&gt;Multi-Class 
 Neural Nets using remote sensing example.&lt;/p&gt;\n&lt;p&gt;By Dr. Haikel Hichri&lt;/p&gt;
 \n&lt;/td&gt;\n&lt;/tr&gt;\n&lt;/tbody&gt;\n&lt;/table&gt;\n&lt;p&gt;&amp;nbsp\;&lt;/p&gt;
END:VEVENT
END:VCALENDAR