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Saturday, January 25 • 10:00am - 10:55am
How to Design Feature Vectors for Model Inputs and

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Statistical learning can and should be applied to many tasks and situations. This hot topic is covered in many talks and classes which talk about machine learning models and the impressive results they can achieve. This is step two, though, the first being able to create datasets which can be used for training. It is not just about the quality and data used but also the representation. An ill-chosen representation can model convergence slow or even impossible, making the model potentially unusuable. The same applies to the representation of the model output, not all output formats are the same.
This talk will talk about the problems with the representation of features and results. The effects of bad choice are shown as well as examples from a number of different problem areas which will show how (sometimes) creative the data scientist has to be to produce a well-performing model.

Speakers
avatar for Ulrich Drepper

Ulrich Drepper

System Research & Data Science, CTO Office, Red Hat
Data Scientist, CTO Office
avatar for Sanjay Arora

Sanjay Arora

Data Scientist
Data scientist at Red Hat



Saturday January 25, 2020 10:00am - 10:55am CET
A112 Faculty of Information Technology Brno University of Technology, Božetěchova, Brno-Královo Pole, Czechia