Machine learning for remote sensing crash course

#machine #learning #remote #sensing #tensorflow
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The main goal of this workshop is to provide a training course for remote sensing researchers and practitioners in Saudi Arabia, from both government and private sectors, on how to best use deep learning Tensorflow API. The course will be taught using online material and computational resources and by experts in machine learning. The participants need to bring their own laptop to access the learning material. 

The workshop will answer questions like:

  • How does machine learning differ from traditional programming?
  • What is loss, and how do I measure it?
  • How does gradient descent work?
  • How do I determine whether my model is effective?
  • How do I represent my data so that a program can learn from it?
  • How do I build a deep neural network?

Participants 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.

Useful background for taking the course:

You need to bring a laptop to do the practical exercises in this workshop.

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 logarithms and derivatives is helpful, but not required.)

Proficiency in programming basics, and some experience coding in Python. Programming exercises in Machine Learning Crash Course are coded in Python using TensorFlow. No prior experience with TensorFlow is required, but you should feel comfortable reading and writing Python code that contains basic programming constructs, such as function definitions/invocations, lists and dicts, loops, and conditional expressions.


  Date and Time

  Location

  Hosts

  Registration


  • Start time: 30 Apr 2019 08:00 AM
  • End time: 01 May 2019 02:00 PM
  • All times are (UTC+03:00) Riyadh
  • Add_To_Calendar_icon Add Event to Calendar
  • Po Box 51178, Computer Engineering
  • College of computer and info sciences
  • Riyadh, Ar Riyad
  • Saudi Arabia 11543
  • Building: 31 (male section)/ 6 for female section
  • Room Number: Auditorium G56(male side)/G21(female side)
  • Click here for Map
  • Contact Event Host

  • ybazi@ksu.edu.sa

    najlan@ksu.edu.sa

  • Co-sponsored by King Saud University
  • Starts 12 April 2019 07:15 PM
  • Ends 28 April 2019 11:55 PM
  • All times are (UTC+03:00) Riyadh
  • No Admission Charge

  Speakers

Haikel Hichri, Nassim Ammour, Haidar Almubarak, M. Alrahhal Haikel Hichri, Nassim Ammour, Haidar Almubarak, M. Alrahhal of King Saud University

Topic:

Deep Learning with Tensorflow

Biography:

Haikel Hichri, Nassim Ammour, M. Alrahhal, Haidar Almubarak are with the advanced Lab for Intelligent Systems Research (ALISR).

Email:

Address:Po Box 51178, Computer Engineering, College of computer and info sciences, Riyadh, Ar Riyad, Saudi Arabia, 11543

Yacoub Bazi Yacoub Bazi of King Saud University

Topic:

Remote sensing research and deep learning

Introduction to remote sensing

deep learning in remote sensing research problems 

Biography:

Yakoub Bazi (S’05–M’07–SM’10) received the State Engineer and M.Sc. degrees in electronics from the University of Batna, Batna, Algeria, in 1994 and 2000, respectively, and the Ph.D. degree in information and communication technology from the University of Trento, Trento, Italy, in2005. From 2000 to 2002, he was a Lecturer with the University of M’sila, M’sila, Algeria. From January to June 2006, he was a Postdoctoral Researcher with the University of Trento. From August 2006 to September 2009, he was an Assistant Professor with the College of Engineering, Al-Jouf University, Al-Jouf, Saudi Arabia. He is currently an Assistant Professor with the Advanced Laboratory for Intelligent Systems Research, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia. He is a Referee for several international journals. His research interests include machine learning, pattern recognition, and evolutionary computation methodologies applied to remote sensing images and biomedical signal/images(change detection, classification, and semi-supervised learning)

Email:

Address:Po Box 51178, Computer Engineering, College of computer and info sciences, Riyadh, Ar Riyad, Saudi Arabia, 11543





Agenda

Day 1 (Tuesday, 30 April 2019)

Time

Activity

8:00 – 8:30

Registration and breakfast

8:30-9:00

Introduction to GRSS Saudi Arabia chapter

By Dr. Haikel Hichri

9:00-9:30

Introduction to remote sensing

By Dr. Haikel Hichri

9:30-10:00

Machine learning concepts, Introduction to ML, Framing.

By Dr. Haidar Almubarak

10:00-10:15

Coffee break

10:15-11:45

Descending into ML, Reducing Loss.

By Dr. Haidar Almubarak

11:45-12:15

Salat/Namaz break

12:15-13:30

First Steps with TF, Generalization, Training and Test Sets.

By Dr. Nassim Ammour

 

Day 2 (Wednesday, 1 May 2019)

Time

Activity

8:00 – 8:30

Breakfast

8:30-10:00

Logistic Regression, Classification.

By Dr. Alrahhal

10:00-10:15

Coffee break

10:15-11:45

Neural Networks, Training Neural Nets.

By Dr. Yacoub bazi

11:45-12:15

Salat/Namaz break

12:15-13:30

Multi-Class Neural Nets using remote sensing example.

By Dr. Haikel Hichri