Episode 6: Regression and complexity
25 May, 2021
#podcast #T1E6
In this chapter we introduce one of the basic problems that Artificial Intelligence tools try to solve: regression. Regression models try to predict a certain variable using other variables as input, so that the resulting model reveals the relationship between them. Deep Learning deals with these problems using neural networks, which are sometimes very complex. To shed light on regression and the complexity of these models, we interviewed Pablo Mesejo, researcher at DaSCI.
Pablo Mesejo holds a Master’s degree and a PhD in Computer Science from the Universities of La Coruña and the University of Parma in Italy, where he was contracted for a Marie Curie ITN project: He has also had postdoctoral stays at the University d’Auvergne, in France, and at the Mistis team of Inria, something like the CSIC of France. His research interests include computer vision, machine learning and computational intelligence techniques applied mainly to biomedical image analysis problems.
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