Machine Learning Group 2021

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WS1 – Pilot studies: from Idea to Valid solutions

Theme 1: Coding and Classification

1.1. Apply ML techniques to classification and aggregation web scraped price data, IBGE Brazil - Vladimir Miranda

1.2. Using Big Data Tools and Machine Learning Techniques to Assign Classification of Individual Consumption by Purpose (COICOP) Categories, Turkey Statistical Institute (TURKSTAT) - Mustafa Karamavus

1.3. Code Classification using Multilingual Transformer-based Models, Danish Business Authority - Casper Eriksen

1.4. Coding and Classification: Automated coding of classifiers as a shared service, INE Chile - Klaus Lehmann

Theme 2: ImputationTheme 3: Imagery Analysis

1.6. Estimating Malaysia Rubber Plantation Area Productivity Using Satellite Imagery and Machine, Department of Statistics Malaysia - Rajkumar a/l V.Nagarethinam

1.7. Feasibility study of Satellite Imagery Analysis for Wealth Index Development in Indonesia, BPS Statistics Indonesia - Arie Wahyu

Theme 4: Modelling for Estimation

1.8. State level expenditure estimates based on ML techniques, U.S. Bureau of Labour Statistics - Yezzi Lee; Clayton Knappenberger

Theme 5: Transferring Knowledge and Experience

1.9. Replicating successful data science projects across NSOs, Statistics Flanders - Michael Reusens

Theme 6: Route Optimisation

1.10. Route Optimisation through genetic algorithm, INE Chile - Jose Bustos

WS2 – From Valid Solution to Production

Activities

2.1. Automated production tool to code IMF member state time series data using ML algorithms, International Monetary Fund - Marco Marini

2.2. Deployment of a Data Lake architecture to put into production data science projects, INEGI Mexico - Abel Coronado

2.3. Design and assess a whole workflow to enable Natural Language Processing and Machine Learning methodologies to be integrated into a continuous production process, INEGI Mexico - Jael Pérez; Alejandro Ruiz

2.4. A technical platform that supports the whole machine learning process and thereby ensures the quality of that process and in the end contribute the overall quality of the statistical output, Statistics Sweden - Alexander Thorell

WS3 – Data Ethics and Governance

3.1 The establishment of a set of ethical principles to provide a clear framework to enable ethical use of Machine Learning for research and statistics, UK Statistics Authority - Lily O'Flynn; Simon Whitworth

WS4 – On The Quality of Training Data

4.1 Identifying the circumstances under which an ML model should be retrained in order to maintain the predictive power and quality of the model, Statistics Finland - Riitta Piela; Rok Platinovsek

WS5 – On The Quality Framework for Statistical Algorithms

5.1 Explore dimensions of QF4SA in a consolidated project to analyse an output based on a set of standard metrics and procedures
INEGI Mexico - Jose Jimenez; Alejandro Ruiz

How to join the Group ?

Do you have any ML topic you are interested in working together with peers? Do you have any issue (technical, strategic, organisational) you want to discuss with other NSOs? Contact ML2021 (ML2021 at ons dot gov dot uk), copying UNECE (choii at un dot org) if you want to join the Machine Learning 2021 Group!

If you are a member of the Global Network of Data Officers and Statisticians, you can follow us from the ML for Official Statistics group in the Network (see quick guide on how to join the Global Network).

For terms of reference, click this link.

Progress Update

Resources

Machine learning competency in context of Big Data training and human resources (from UN Global Working Group on Big Data Task Team on Skills and it is relevant to the community)