Computational Data Analysis (Lecture)

Instructors: Illia Horenko

Academic Programs: Master of Science in Informatics: Applied Mathematics and Computational Science

Workload: 6 ECTS

Description

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).

Professor

 
Area
Computational Science

Use computers to simulate the world to answer «What would happen if ...»? More...

 
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