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Interpretable and Interactive Machine Learning

Interpretable and Interactive Machine Learning

Machine Learning has positioned itself as a hot topic and almost as a synonym to data analysis. However, most of the current real world ML systems use models as black-boxes. Given this, the big question that arises is what about if model performance is not the unique requirement to be fulfilled? To address this, two new sub-fields of research have been proposed to help users to interact and understand ML models, and by this opening the black-box Interactive ML and Interpretable ML. This master thesis project is developed in the context of these sub-fields, with applications to Exploratory Data Analysis (EDA) supported by the t-SNE algorithm, a common technique for dimensionality reduction of high-dimensional data. The contributions of this project are (1) provide a set of guidelines for designing more user-centric ML systems in terms of interactivity and interpretability and (2) design and develop of a visual tool to interact and interpret t-SNE models for performing EDA. The proposal is evaluated in two case studies, the animals dataset and the urban environment dataset from SALURBAL project.


José Tiberio Hernández
Honorary Professor
John Alexis Guerra Gómez
Assistant Professor