Third Machine Learning Workshop for Engineers using MATLAB

AUDITORIUM 0215 BW BUILDING 2 AND 3 Level: 0

 

Recording of the workshop

Zoom Link

Links for Material: Demo Scripts  Raw Data  MAT Data Ibex_HandsOn

Registration

Gate Pass Form          (Only for non-KAUST attendees attending in person)

Agenda           

 

The KAUST Supercomputing Core Laboratory (KSL) is organizing a 1-day workshop on Machine Learning for Engineers using MATLAB.  This hybrid event is organized in conjunction with CES, exclusive partner of MATLAB & Simulink in the Middle East.

About this event

This event will cover the fundamentals of machine learning using MATLAB and will give the participants hands-on experience in applying deep learning to an interesting engineering problem – Seismic Facies Classification.

Who can attend?

This workshop is open to all engineers, researchers, students and managers, who are working at KAUST or any other educational institute or industrial company in Saudi Arabia. Non-KAUST attendees may be able to attend in person if they fill out the gate pass form in time and obtain confirmation email from us. Remote attendees will be receiving zoom link from us after registering.

This workshop is targeted at engineers & researchers who have limited or no experience of applying Machine Learning to their specific problems. Attendees with no background in MATLAB and/or machine learning can certainly benefit from this workshop.

To register

Click here to register for this online workshop. You will be receiving more information via emails after your register.

Important: Before attending this workshop

To get the most out of this workshop, we encourage you to do the following free self-paced online certificate, before attending, and/or review the training material from the previous workshop. 

For more information, visit the workshop website or contact us at training@hpc.kaust.edu.sa.

We are looking forward to meeting you!

Dr. Rooh Khurram

KAUST Supercomputing Core Lab

Flavio Pol

Application Engineer

CES – exclusive partner of MathWorks in the Middle East

 

Speakers: Learn more about us

Workshop Zoom Link: Zoom Link

Training Material: Demo Scripts  Raw Data  MAT Data

Presentation Slides: Coming Soon

Feedback Form: Click here for feedback

Recording of the Workshop: link

MATLAB Download Instructions:

  • KAUST attendees should install MATLAB on their laptops from here
  • Non-KAUST cannot use KAUST licenses. They should have it installed on their laptops. We might be able to provide some light weight web-version access on the morning of the event.

MATLAB Desktop Required Products

  1. MATLAB
  2. Statistics and Machine Learning Toolbox
  3. Deep Learning Toolbox
  4. Parallel Computing Toolbox
  5. Image Processing Toolbox
  6. Signal Processing Toolbox
  7. Wavelet Toolbox

Previous Events:

Upcoming Events:

  • 4th Applied Deep Learning Workshop for Engineers using MATLAB, TBA (participants will receive announcement via email)

Seismic Facies Classification:

With the dramatic growth and complexity of seismic data, manual annotation of seismic facies has become a significant challenge. One of the challenges lies in classification where interfaces between different rock types inside the Earth are delineated in a seismic image i.e., dividing the subsurface into regions that can be classified as distinct geologic facies. Delineating these facies requires months of efforts by geoscientists.

Can AI algorithms solve this? Can they be trained to recognize distinct geologic facies in seismic images, producing an interpretation that could pass for that of an expert geologist, or be used as a starting point to speed up human interpretation? Recently, deep learning algorithms (particularly CNNs) have been used to simplify this task.

In this workshop, we walk through how MathWorks helped solve this challenge with a unique and innovative approach. We demonstrate the advantage of using advanced signal processing techniques to pre-process signals before feeding them to deep learning algorithms. Our approach combines maximal overall discrete wavelet transform with recurrent neural networks (RNN) to improve the automated seismic facies analysis. This proposed framework generates more accurate results in a more efficient way. In addition, you will learn the following:

  1. How MATLAB simplifies the application of advanced techniques like wavelets through interactive apps.
  2. Creating Deep Learning models with just a few lines of MATLAB code
  3. Exploring a seismic volume with Volume Viewer App
  4. Accelerate algorithms on NVIDIA® GPUs or the cloud without specialized programming or extensive knowledge of IT infrastructure.

For more details about the methodology, please refer to this MathWorks blog.

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Collaborative