The importance of datasets for machine learning and data science

Day 1, 9:30-10:30 am (lecture)
Amelia Taylor

🐦 @AT_poly_AI

The growth in the use of technology is evident in all sectors of human activity, i.e. education, business, social life and government. This is due to many factors. Two that are most often quoted are ‘the availability of massive datasets’ and ‘cheap computing power’. Data science aims at applying a scientific approach to extract meaning and insights from data. Machine learning aims at developing algorithms that learn from data. Good data sets are crucial to data science and machine learning, because small, messy and incorrect data can waste time and resources and can lead to models that produce meaningless and biased results. This talk will look into the importance of data sets with case study from the Malawi context. It will overview the tasks needed for developing data sets and it will demonstrate how datasets, data structures and algorithms affect each other.

Speaker biography

Amelia Taylor (PhD) is a lecturer in Artificial Intelligence at the University of Business and Applied Sciences (MUBAS) – former University of Malawi, The Polytechnic. She teaches Artificial Intelligence, Computational Intelligence and programming modules. In addition, she teaches and supervises MSc and PhD students. She was the internal supervisor of the first graduate of the PhD program run by the university. She is currently leading the development of MSc programs in the Computing and Information technology department. Dr Taylor is currently conducting research in Natural Language Processing, AI for Legal text and data mining and visualisation for health data. She is the main organiser of IndabaX Malawi, a machine learning conference in Malawi.

Dr. Taylor graduated with a PhD in Mathematical Logic from Heriot-Watt University. After that she worked as a research assistant on a project with Heriot-Watt University and the Royal Observatory in Edinburgh, aiming at developing an intelligent query language for astronomical data. From 2006 to 2013, Amelia Taylor worked in finance in the City of London and Edinburgh - she built risk models for asset allocation and liability-driven investments. In 2013 she joined the University of Malawi, after her family’s relocation to Malawi.