Head of the department: Doctor of Physics and Mathematics. Sciences, Professor Lebedev Andrey Vladimirovich

Leading specialist in supporting the educational process: Sorokoletova Olga Nikolaevna

Teaching staff: 6 professors, 10 associate professors

mathematical analysis;

complex analysis;

dynamic systems;

linear and nonlinear integral equations;

qualitative theory of differential equations;

general theory of partial differential and functional differential equations;

approximate methods for solving operator equations;

stochastic differential equations;

control theory and mathematical economics.

Disciplines assigned to the department

optimization methods;

calculus of variations;

operations research;

statistical methods in economics;

combinatorial modeling and operations research.

convex analysis;

ordered linear spaces;

mathematical foundations of consumption theory;

economic and mathematical models;

chapters on multivariate analysis and economic applications;

elements of queuing theory;

stochastic analysis of financial markets;

information theory;

probabilistic models of market prices;

mathematics and mathematical models;

inventory management models.

Floyd’s algorithm with step-by-step implementation in Python.

Multiple regression and correlation.

Amenable groups.

Time series analysis using the R language.

Pairwise regression and correlation.

Fourier series.

Geometric and analytical properties of spaces with scalar product.

Fractals and their properties.

Hardy spaces in the half-plane.

Hardy spaces in a circle.

Dynamic programming.

Random walk process. Tasks and applications.

Functions of limited variation.

Dijkstra’s algorithm with step-by-step implementation in JS.

Banach-Tarski paradox.

Random walk process and neural networks.

Cones in infinite-dimensional space.

Code sequences.

Models of financial mathematics.

Carleson measures.

Genomic sequences.

Closed operators.

Algorithms for finding the shortest routes.

Generalized Leontief model

Information theory. Information entropy.

Luzin approximation in Gaussian spaces.

-Nilpotent and -soluble groups and their classes.

Finding the shortest paths from a fixed vertex of the graph to the rest.

Ideal spaces.

Network models.

Hausdorff measure and dimension.

Ideal spaces.

Combinatorial foundations for constructing probability spaces.

Classical probability-theoretic models.

Fractional integrals

Expansion of entire functions into an infinite product

Statistical Machine Learning

Galois theory

Linear and nonlinear mathematical

Network models

Trigonometric series with decreasing coefficients

Green’s formula

Financial risk analysis

Examples of solving Bayesian games

Dynamic programming problems

Voting systems

Kurzweil-Henstock integral

Reinforcement learning and its applications

Network models

Hardy’s inequality

A Rigorous Approach to Defining Elementary Functions

Optimization methods in machine learning

Statistical analysis of homogeneity

Methods of entropy analysis in machine learning

Wiener process. Theory and applications

Inductive limits of topological spaces

Modeling and filtering noise in signals

Models of financial mathematics

Algebraic and topological properties of linear operators in normed spaces

Classical inequalities and functional analysis

Network planning, building a network model, Gantt chart