Mathematical Methods of Data Analysis

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Department of Applied Mathematics

Synopsis:

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

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The course is a part of the following study plans: