Introduction to Machine learning in social science: Best practices for developing training data
Date:
07/10/2025 - 15/10/2025
Organised by:
CLOSER (University College London)
Presenter:
Dr Wing Yan Li and Dr Chandresh Pravin
Level:
Entry (no or almost no prior knowledge)
Contact:
CLOSER Administrative and Events Assistant, Becky England, becky.england@ucl.ac.uk
Venue: Online
Description:
Join us for our latest webinar, taking over two 90-minute sessions (7 & 15 October, 15:00-16:30) to introduce participants to machine learning in social science and best practices for developing training data.
Led by Postdoctoral Research Fellows, Wing Yan Li and Chandresh Pravin (University of Surrey) this workshop will cover a typical “packaging” of data to train and evaluate models.
We will explore various aspects that contribute towards good practice for creating quality training datasets, including
- exploratory data analysis,
- the selection of evaluation metrics,
- model selection, and
- model evaluation.
Conventionally, models are evaluated quantitatively, as represented by the appropriate metrics, and qualitatively. While it might be tedious to qualitatively analyse all the samples, random sampling could be problematic. In the section covering model evaluation, workshop participants will be introduced to the problem of data biases and gaps. By bridging technological approaches with social science research needs, this workshop offers an exploration of data transformation techniques that enhance research reproducibility and computational analysis capabilities.
Cost:
Free
Website and registration:
Region:
Greater London
Keywords:
Data Collection, AI and machine learning
Related publications and presentations from our eprints archive:
Data Collection
AI and machine learning