Mathematical Methods of Data Analysis

Course guarantor:

Ivan Nagy

Lecturer:

Ivan Nagy, Pavla Pecherková, Evženie Uglickich

Tutor:

Ivan Nagy, Pavla Pecherková, Evženie Uglickich

Supervisor:

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

Requirements:

Syllabus of lectures:

Syllabus of tutorials:

Study Objective:

Study materials:

Note:

Time-table for winter semester 2024/2025:

Time-table is not available yet

Time-table for summer semester 2024/2025:

Time-table is not available yet

The course is a part of the following study plans: