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Sequences are an unusual type of data in many ways. Sequence data breaks all the usual modelling assumptions of independence and strongly resists attempts to evaluate results tidily.
Subsequences and intervals have a beginning, a middle and an end, and are not easily reducible to a nice round number. But, in being part of the awkward squad, the challenges of interpreting sequence data suggest ways in which results from machine learning, modelling and AI in general should be better explained. I'll give examples from my work with DNA and with time series.
About the speaker
Dr Amanda Clare, Computer Science Department, Aberystwyth University
Amanda Clare is a Senior Lecturer in Computer Science at Aberystwyth University and currently holds a Royal Academy of Engineering Industrial Fellowship. Her research is in data analysis and data science, and in particular in bioinformatics and natural language processing/text analysis, but also the analysis of sequences in general, including time series. She is interested in formalising components and variation, from nucleotides to genes, genomes and metagenomes.
She investigates interesting patterns in data using machine learning, statistics, visualisation and algorithms. She is a member of the BCS and a fellow of the HEA.
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