Introduction to Machine Learning

Mr Chris Watkins1

1Csiro, Clayton South, Australia


As CSIRO embraces the transition into the technological age it has spawned a variety of digital initiatives designed to accelerate researchers’ application of modern digital advances to their technical domains. One such initiative is the CSIRO Data School program which has been designed to equip scientists with the tools necessary to apply defensible, reproducible data analytics to unique scientific datasets. A small part of the Data School is a two day workshop designed to introduce participants to the opportunities and challenges offered by the application of modern Machine Learning (ML) techniques.

Effectively broken into four modules, the workshop aims to first introduce ML and demystify the associated hype, expose students to the engineering effort required to ingest, inspect and cleanse raw data, provide a light overview of some useful ML approaches and equip students with the ability to verify and validate the results produced by their ML pipeline. The workshop uses Python as it’s delivery vehicle and employs The Carpentries’ template to lesson design and structure.

This workshop proposal is somewhat flexible and can be tailored to meet the anticipated attendee demand, ranging from a single half day workshop and potentially expanding to the full two day version. The content itself does not anticipate being able to replace three years of intense ML study but only provide participants with a more grounded (hype-free) overview of the field to help them identify when, where and how ML can be applied to their technical domain, in a defensible scientific manner.


Research software engineer with the Scientific Computing team at CSIRO. Chris works mostly with machine learning applications to scientific problems.


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