The goal of this course is to provide methods and tools for modeling distributed intelligent systems as well as designing
and optimizing coordination strategies. The course is a well-balanced mixture of theory and practical activities. ...
This course aims to introduce the basic principles of machine learning in the context of the digital humanities. We will cover both supervised and unsupervised learning techniques, and study and implement methods to analyze diverse data types, such as imag ...
This course introduces the foundations of information retrieval, data mining and knowledge bases, which constitute the foundations of today's Web-based distributed information systems. ...
This course develops, within the urban domain, a critical review of challenges/opportunities of the One Health concept, based on the inexorable link between human, animal, and ecosystem health. ...
This course provides in-depth understanding of the most fundamental algorithms in statistical pattern recognition or machine learning (including Deep Learning) as well as concrete tools (as Python source code) to PhD students for their work. ...
This course aims to provide graduate students a thorough grounding in the methods, theory, mathematics and algorithms needed to do research and applications in machine learning. The course covers topics from machine learning, classical statistics, and data ...