Instructors: Illia Horenko
Workload: 6 ECTS
This course represents the last part of the `Computational Methods`-trilogy, the main aim of this course is to give a wide practical overview of data mining, time series analysis and inverse problems from the viewpoints of applied mathematics, statistics and high performance computing strategies. The topics considered in the course include: clustering methods; concepts from information theory; generalized linear models for statistical regression of discrete processes; statistical methods of inference beyond the homogeneity/stationarity assumptions (local kernel methods, FEM-based methods). Parametric vs. non-parametric methods. Applications of considered methods and tools will be illustrated on realistic data sets (e.g., problems from fluid dynamics, climate/weather research, biophysics, sociology, finance/insurance).