(1/1 academic year)
Statistical Learning and Data Mining
1: Data mining projects and classification of data systems and model types data mining.
- Catalogue of technical resources: Artificial Intelligence, Learning Machine Learning, Statistical Learning, Data Mining and the new Big Data challenge.
- The KDD process (Knowledge Discovery in Databases).
- Recommendation systems.
- Data analysis with cubes and mining models.
- Evaluation and selection of models. Confusion matrix, metrics, costs. Curves ROC
The objective of this course is to introduce students to the basic concepts of statistical learning, understanding as such the series of techniques that allow the description and modeling of complex data sets and the automatic procedures framed under the category of “data mining”, also known as Knowledge Discovery in Databases, KDD, which serve to extract relevant and generally implicit and unknown information from massive data, through mechanisms that detect patterns and regularities in such data and that can be used to predict responses to new situations, detect the key fields that determine the characteristics of the problem, perform segmentations of automatically, launch forecasts, detect exceptions, propose scenarios; in short, anticipate the future objectively and based on real data.