Objective: introducing students to computational management of data, in particular the characterization of an information system, data modeling, design and management of databases, including non-traditional ones (eg, unstructured documents, spatial data, biological data , multimedia data), to the fundamentals of distributed data and to methodologies and techniques for the management and analysis of big data.
Objective: Introduce the students to the machine learning fundamentals, to the main techniques on supervised learning, and to the principal application domains. Present evolutionary calculation. The course explains how to design, develop and evaluate simple ML-based end-to-end systems and, at the same time, how to describe their operations.
Objective: introduce the student to the principles of learning from data based on statistics, and to the scientific treatment of data to obtain new and reproducible knowledge. Some of the main supervised and unsupervised statistical learning techniques are presented.
Objective: presenting the most important conceptual aspects of information retrieval systems and both principles and techniques for data visualization.
The course will introduce the student to the main problems, data sources, and models in Geophysics.
The course will introduce the student to data sources, problems, main concepts and models in Earth Science, with a focus on analytics using these data.
The course will introduce the student to the main ideas, problems, models and data analytic methods in oceanography and in marine ecology.
The course will introduce students to main ideas, problems, data sources and models in georesource identification and management and in energy production.
Objective: presenting advanced machine learning techniques, with a focus on Bayesian methods.