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Dates

 -  

1pm – 4pm (AEST)

Location

Online (Zoom).


Change of venue

Due to evolving COVID-19 restrictions, this event will now be held online. Zoom meeting details will be sent to you in the week before the event. Please register on Eventbrite and check your email for more information.




About the Workshop Series

NCI and MathWorks are offering three hands-on workshops with a focus on MATLAB, AI and parallel computing. Limited spaces are available, so please reserve your placement by registering. Click here to register.

Prework:

  • This workshop is for NCI users who have a MATLAB license. For more information about Matlab license, please check our webpage about Matlab license supported groups. See the following FAQ for instructions on how to create a MathWorks account: https://www.mathworks.com/videos/create-a-mathworks-account-using-a-matlab-portal-1600159919958.html
  • Attendees should have a basic level of understanding of MATLAB syntax. The FREE online course MATLAB OnRamp would be a recommended prerequisite for any new users of MATLAB.

19 July, 1pm-4pm AEST: Machine Learning with MATLAB

Overview

Are you new to machine learning and want to learn how to apply these techniques in your work?  Machine learning is a data analytics technique that teaches computers to do what comes naturally to humans and animals: learn from experience. Machine learning algorithms use computational methods to “learn” information directly from data without relying on a predetermined equation as a model.

Or did you try Machine Learning, and it felt like a one-time exercise? Using MATLAB, engineers and other domain experts have deployed thousands of applications for predictive maintenance, sensor analytics, finance, and communication electronics.

Highlights

  • In this hands-on workshop, you will use MATLAB to:
  • Learn the fundamentals of machine learning and understand terms like “supervised learning”, “feature extraction”, and “hyperparameter tuning”
  • Build and evaluate machine learning models for classification and regression
  • Perform automatic hyperparameter tuning and feature selection to optimize model performance
  • Apply signal processing and feature extraction techniques
  • Deploy machine learning to production environments either using automated C/C++ code or Compiler (demonstration only)

About the Presenter

Emmanuel Blanchard is an application engineer at MathWorks who first joined the company as a training engineer. He taught several MATLAB, Simulink and SImscape courses as well as specialized topics such as machine learning, statistics, optimization, image processing and parallel computing. Prior to joining MathWorks, he was a Lecturer in Mechatronic Engineering at the University of Wollongong. He holds a PhD in Mechanical Engineering from Virginia Tech. He also worked as a Systems / Controls Engineer at Cummins Engine Company and as a research assistant in several research institutions in California and Virginia. 

Slides and recording

PDF: MATLAB_MachineLearningWorkshop.pdf

Video: https://youtu.be/CXualydgUKI



20 July, 1pm-4pm AEST: Deep Learning with Images and MATLAB

Overview

Please join MathWorks and learn how to get started with MATLAB for Deep Learning with Images.  In this hands-on workshop, we will introduce you to fundamentals of Deep Learning with Images. You’ll have the opportunity to try out specific examples using MATLAB tools.  The hands-on component of the workshop will be run via MATLAB Online – so attendees do NOT need to have MATLAB locally installed on their computers. 

Highlights

  • Learn the Deep Learning image classification workflow in MATLAB
    • Image Data management
    • Network assembly, training
    • Experiment management
  • Create a Convolution Neural Network (CNN) from scratch
    • Programmatically and using APPs
  • Explore how to access and adjust pretrained models (transfer learning)
  • Explore how to evaluate the network and improve its accuracy

About the Presenter

Bradley Horton is a member of the Academic Customer Success team at MathWorks, helping faculty members better utilize MATLAB and Simulink for education and research. Bradley has supported and consulted for clients on projects in process control engineering, power systems simulation, military operations research, and earthquake impact modelling. Before joining MathWorks, Brad spent 5 years as a systems engineer with the Defence Science & Technology Organization (DSTO) working as an operations research analyst. Bradley holds a B.Eng. in Mechanical engineering and a B.Sc. in Applied mathematics.

PDF: bh_ANU_2021_DeepLearningWorkshopSlides.pdf

Video: https://youtu.be/qp-Ivo1EQpQ



Overview

Do you have code in MATLAB or a model in Simulink that you need to accelerate?  Do you need to run large scale Monte Carlo simulations?  This workshop is designed to walk through the steps, in a hands on format, to take your existing code and transform such that it is suitable for running on large scale parallel compute facilities such as those hosted at the NCI.  We address the toughest issues you will face such as adapting variables to parallel loops and data management so that you are ready to simply click submit for your jobs. The workshop will be run in a hands on format working through a series of examples to step you through the process of taking a section of code from serial to parallel.

About the Presenter

Peter Brady is an application engineer with MathWorks striving to accelerate our customer’s engineering and scientific computing workflows across maths, statistics, and machine learning. Prior to joining MathWorks, Peter worked in computational fluid and thermodynamics as well as high-performance computing for a number of defence and civil contractors as well as a few universities. He has worked in fields as diverse as cavitation, wave/turbulence interactions, rainfall and runoff, nano-fluidics, HVAC and natural convection including scale out cloud simulation techniques. Peter holds a Doctorate in free surface computational fluid dynamics and a Bachelor of Civil Engineering both from the University of Technology Sydney.

PDF: IntroToParallelComputingWithMATLABatNCI.pdf

Exercise files (in ZIP): files.zip

Video: https://youtu.be/8Uz9yuqUS8o



Who can attend: 

This workshop is for NCI users who have a MATLAB license. For more information about Matlab license, please check MATLAB.

If you are located outside of ACT, please list your location and registered as EOI so that we will see what we can do in the future to accomodate people remotely. 



Privacy Collection Notice:

Your registration information for this event is being collected by NCI Australia, Australian National University (ANU).

This event is co-hosted by MathWorks and a copy of your registration details will be provided to them for future event planning and marketing purposes.

The information is being collected, with your consent, in order to complete your registration for the MathWorks Workshop Series. The information will be used by authorised staff for the purpose for which it was collected, and any other purpose which you have expressly consented to.

You can withdraw your registration any time by contacting training.nci@anu.edu.au 

If you do not provide all the information that is requested on this form, it may not be possible for us to ensure appropriate arrangements for the event.

The ANU is committed to protecting personal information provided by you in accordance with the Privacy Act 1988 (Cth) and the information will be protected against unauthorised access and use. All information collected by the University is governed by the ANU Privacy Policy.

For further information about how the University deals with personal information, please refer to the ANU Privacy Policy or contact the ANU Privacy Officer at privacy@anu.edu.au.

You may access or request correction of any personal information you have provided to ANU by contacting privacy@anu.edu.au.

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