by Prof. José Camacho
Do you have complicated data? Difficult to understand? With thousands of variables/observations? Missing data? Time series? Multiple sources? Big Data? … There is a bunch of Machine Learning (ML) methods that can do difficult tasks for us (like classification, modelling and prediction) using a black-box approach over data, deep learning being a popular example. Unfortunately, ML should never been applied blindly, because the quality of the model depends on the quality of the data we feed it with. Matrix factorization and associated visualizations represent a powerful tool to extract insights from a data set. The talk will discuss the basic importance of looking at data, regardless its complexity, and provide a workflow to do so
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