Izabela Moise: Catalogue data in Spring Semester 2017

Name Dr. Izabela Moise
DepartmentHumanities, Social and Political Sciences

851-0585-38LData Science in Techno-Socio-Economic Systems Restricted registration - show details
Number of participants limited to 70.

This course is thought be for students in the 5th semester or above with quantitative skills and interests in modeling and computer simulations.

Particularly suitable for students of D-INFK, D-ITET, D-MAVT, D-MTEC, D-PHYS
3 credits2VI. Moise, E. Pournaras
AbstractThis course introduces how techno-socio-economic systems in our nowadays digital society can be better understood with techniques and tools of data science. Students shall learn the fundamentals of data science, machine learning, but also advanced distributed real-time data analytics in the Planetary Nervous System. Students shall deliver and present a seminar thesis at the end of the course.
ObjectiveThe goal of this course is to qualify students with knowledge on data science as a way to understand complex techno-socio-economic systems in our nowadays digital societies. This course aims to make students capable of applying the most appropriate and effective techniques of data science under different application scenarios. The course aims to engage students in exciting state-of-the-art scientific and collaboration platforms such as the Planetary Nervous System. The course shall increase the awareness level of students about the challenges and open issues of data science in socio-technical domains such as privacy. Finally students have the opportunity to develop their writing, presentation and collaboration skills based on a seminar thesis they have to deliver and present at the end of the course
851-0585-45LMachine Learning and Modelling for Social Networks Restricted registration - show details
Number of participants limited to 50.
2 credits1VO. Woolley, N. Antulov-Fantulin, I. Moise, L. Sanders
AbstractThis mini-course covers computational and statistical methods to characterize the structure and dynamics of complex social networks. We cover methods such as clustering, classification, spectral analysis and Montecarlo and also specific applications to social network data and spreading processes on these networks. We discuss current research and ethical questions raised by applications.
ObjectiveThis advanced course will give students insight into the questions that can be answered analyzing network data and into the related challenges. They will be exposed to the main methods that can be used to tackle these questions and learn about the shortcomings of these current methods. We will also raise students awareness of some of the ethical questions raised, mainly in the realm of privacy, by the types of data collected and the influence on individual behavior that can be achieved through technologies built on the methods presented in class. Students will be encouraged to apply their knowledge to a specific network dataset by producing a research proposal.
Prerequisites / NoticeStudents must be in their 5th semester or more advanced.
Knowledge of basic: linear algebra, differential equations, probability, statistics and programming.