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
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
calculus of variations;
statistical methods in economics;
combinatorial modeling and operations research.
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;
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.
Time series analysis using the R language.
Pairwise regression and correlation.
Geometric and analytical properties of spaces with scalar product.
Fractals and their properties.
Hardy spaces in the half-plane.
Hardy spaces in a circle.
Random walk process. Tasks and applications.
Functions of limited variation.
Dijkstra’s algorithm with step-by-step implementation in JS.
Random walk process and neural networks.
Cones in infinite-dimensional space.
Models of financial mathematics.
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.
Hausdorff measure and dimension.
Combinatorial foundations for constructing probability spaces.
Classical probability-theoretic models.
Expansion of entire functions into an infinite product
Statistical Machine Learning
Linear and nonlinear mathematical
Trigonometric series with decreasing coefficients
Financial risk analysis
Examples of solving Bayesian games
Dynamic programming problems
Reinforcement learning and its applications
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