This session studies specific challenges that Machine Learning (ML) algorithms have to tackle when faced with Big Data problems. These challenges can arise when any of the dimensions in a ML problem grows significantly: a) size of training set, b) size of test set or c) dimensionality. The studies included in this edition explore the extension of previous ML algorithms and practices to Big Data scenarios. Namely, specific algorithms for recurrent neural network training, ensemble learning, anomaly detection and clustering are proposed. The results obtained show that this new trend of ML problems presents both a challenge and an opportunity to obtain results which could allow ML to be integrated in many new applications in years to come.