This course aims of exploring concepts and practical implications of interactive computer graphics including image processing, interaction, and data visualization.
This course is focused on providing students with an introductory overview of theoretical and practical aspects of digital signal processing: signal acquisition, sampling, representation with orthogonal functions and digital filtering.
This course aims to explore the fundamentals and key concepts of the time series analysis ranging from conventional statistical to deep-learning-based approaches.
This course aims to explore the fundamentals and key concepts of the field of artificial intelligence, which encompasses logic, probability and continuous mathematics, perception, reasoning and learning. Applications of interest are also addressed.
This course is focused on providing students with elements to: Review and evaluate the form and (in very general terms) the consistency of a scientific article in engineering (especially, those based on applied research and related to data analytics); Make a state of the art making use of bibliographic references, and following a proper conceptual and chronological order; Draft the first version of a scientific results-type paper related to engineering (especially, with data analytics) holding an adequate structure, and making use of computer tools for information search, bibliography management and text editing, and following the recommendations of writing and presentation of scientific production.
This course is focused on providing students with an overview of theoretical and practical aspects of Software Engineering such as: software process, project planning, requirements engineering, design strategies, informal/formal specification, analysis techniques, model-driven development, testing techniques, software product lines, prototyping and presentation.
This course is focused on providing students with an introductory overview of theoretical and practical aspects of the intersection of Artificial Intelligence and Data Mining such as: data representation, pattern recognition, data classification, cluster analysis, quantification of classification performance, and data visualization. In addition, the course covers applications of interest such as biomedical data analysis, digital signal processing, image segmentation and video analysis.