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Mathematical Methods of Data Analysis

Course Code:
11MMA
Academic Degree:
doctoral
Study Programme:
Smart Cities (P0731D010007)
language
czech, english flag
Form of Study:
full-time and part-time
Type of Course:
optional
Course Completion:
credit, exam
Supervisor:
doc. Ing. Ivan NAGY, CSc.
v DB je chyba..
Supervising Department:
Department of Applied Mathematics (16111)
Abstract:
• Introduction of basic notions: system, model • Stochastic model and its estimation (Bayes rule) • Normal and categorical models, estimation • Prediction with dynamic categorical and normal models • State filtration, Kalman filter • Basics of the dynamic programming method for minimization of quadratic criterion • Control of dynamic system with normal and categorical model • Estimation by the method Naive Bayes • Logistic and Poisson regresion • Clustering (data separation, fuzzy clustering, density clustering, hierarchical clustering) • Classification (K-nearest neighbour, Support vector machines) • Decision trees and their use for classification • Recollection and repetition