1pm – 4pm (AEST)
Science Teaching Building, Building 136, Seminar Room 1, Level 3
136 Linnaeus Way, The Australian National University, Canberra, ACT, 2600
Register for this event on Eventbrite: https://www.eventbrite.com.au/e/nci-presents-mathworks-workshop-series-2021-tickets-156992022365
About the Workshop Series
NCI and Mathworks are offering three hands-on workshops with a focus on MATLAB, AI and parallel computing. These workshops will be presented in-person on the ANU campus. Limited spaces are available, so please reserve your placement by registering. Click here to register.
- Attendees will need to bring their own laptops (MATLAB does NOT need to be installed)
- Attendees will need to have a MathWorks account associated with the ANU MATLAB Campus License. See the following FAQ for instructions on how to create a MathWorks account
- 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
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.
- 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.
20 July, 1pm-4pm AEST: Deep Learning with Images and MATLAB
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.
- 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.
21 July, 1pm-4pm AEST: Parallel Computing with MATLAB and Simulink
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.
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.