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Masterclass: Machine Learning

Machine Learning Masterclass – Taught by visiting scientist Kevin Vinsen

April 18 | 2:30-4:30 pm | NW 205

Machine Learning and artificial intelligence (AI) is everywhere; if you want to know how companies like Google, Facebook, Amazon, Apple, and Microsoft extract meaning and insights from massive data sets, this half-day, hands-on course will give you the fundamentals you need. Data Scientists enjoy one of the top-paying jobs, with an average salary of Can$160,000 according to Glassdoor and Indeed. That’s just the average! And it’s not just about money – it’s interesting work too!

If you’ve got some Python 3.7 experience, this course will teach you the basic techniques used by real data scientists and machine learning practitioners in the astronomy world. If you have no Python experience, but can program then attend the Python for Machine Learning half-day course first. This will equip you with the basics you need.

Please register for the masterclass by emailing [email protected] or Dr. Nicola Shaw, as limited spots are available.

Kevin Vinsen is a Senior Research Fellow at the International Centre for Radio Astronomy Research (ICRAR). ICRAR is a joint venture between Curtin University and The University of Western Australia, with funding from the State Government of Western Australia.

Kevin’s main research interests are high performance computing at the ExaScale, developing methods for the automated classification of galaxies using multi-wavelength data, machine learning algorithms and modelling complex systems. Kevin is also working with Algoma University on the AstroQuest (https://astroquest.net.au/) project.

Kevin considers himself one of the luckiest geeks on the planet. He is paid to do what he loves – astronomy and computing with some of the biggest, baddest computers on the planet. No wonder he is always smiling.

If you want to see him speak, check out his TedX Talk: https://youtu.be/7IYtwtwQw8A

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Apr 18 2019


2:30 pm - 4:30 pm


Algoma University
1520 Queen Street E
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