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Have you seen many conference talks explaining machine learning algorithms? But are still unsure how can you use them in the real world? In this talk Evelina will show how she used machine learning methods to improve the user experience in a .NET web application.
As an example, Evelina will use the fssnip.net website which allows simple sharing of F# code samples. The website stores a lot of very useful code snippets, but the original version of the website lacks any search capability and organisation of the code samples is a complete mess.
In the talk, she will walk you through several machine learning algorithms for text processing that we can use to improve the website and make the code samples more accessible and usable. After this talk, you will not only better understand the principles behind several machine learning algorithms, but you will also get a good idea how you can use them in practice to build user-friendly web applications.
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Evelina is a Senior Research Data Scientist at The Alan Turing Institute, the UK's national centre for data science and artificial intelligence. She is passionate about making data science understandable and accessible to everyone. She originally started as a programmer but got interested in machine learning early on and did a mathematics PhD at the University of Cambridge. During her PhD, she worked on Bayesian models for unsupervised learning that integrate heterogeneous biomedical datasets. After that, she worked in cancer research at the MRC Cancer unit in Cambridge, where she focused on helping biologists analyse genomic data.