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 is aimed at providing participants with mid-level training on KDD, unsupervised learning, transfer learning, imbalance data analysis, computer vision, and anomaly detection at both theoretical and practical level.

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.