Coffee and Coding - Foundations of Machine Learning for non-programmers

Led by the UK Office for National Statistics Data Science Campus in partnership with the UNECE HLG-MOS, the ML 2022 Group will build on the outcomes of the work of the UNECE HLG-MOS Machine Learning project and, in consultation with existing and new members, develop and deliver on priority areas of work.

Machine Learning 2022 (ML2022)

This interactive session will discuss the foundation of machine learning and focus on the  theory behind the techniques

The popularity of Machine Learning throughout the scientific community as well as Official Statistics, has been growing as the access to high-powered computing has been increasing. Despite the growing popularity, ensuring that the transition from traditional Official Statistical techniques to those with a Machine Learning basis, should be completed respecting the existing statistical and mathematical knowledge present within communities which they are being applied too. 
During this interactive introductory session, we will discuss the foundations of machine learning, focused on the theory behind these techniques. Introducing them so that no programming experience is required. We will discuss how these different Machine Learning techniques can be applied, evaluated, and interpreted relating them to different Official Statistical problems. 

Session Objectives: 
<ul><li>Begin to understand the utility and purpose of Machine Learning </li><li>Begin to understand the problems which can be tackled with Machine Learning</li><li>Begin to understand the steps involved in addressing a Machine Learning Problem</li><li>Begin to understand how to interpret and evaluate Machine Learning Problem.
</li></ul>
Prerequisite Knowledge: 
<ul><li>Foundational Statistical Knowledge</li><li>No Programming Experience Required, this workshop is theory only. 
</li></ul>

Thomas Wise Biography (ML Foundations Material)
Having recently graduated from completing a master’s in Methodology &amp; Statistics for the Behavioural, Biomedical and Social Sciences from Utrecht University, where he wrote his thesis on comparing machine learning models for the predication of PTSD diagnosis after trauma. Thomas has joined the graduate data scientist programme, working in the Alternative Data Sources division focusing on increasing the application of good coding practices and the classification of clothing items from web scrapped data. 
Thomas has a passion for improving data science, visualisation, and statistical literacy, and regularly provides sessions both inside and outside of the Office for National Statistics prompting this. In his free time, Thomas is a keen gardener, resulting in him living literally in a house-plant jungle.

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Coffee and Coding - Foundations of Machine Learning for non-programmers

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Coffee and Coding - Foundations of Machine Learning for non-programmers

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