Profesor fiable
Este profe tiene una tasa de respuesta rápida muy elevada, ofreciendo así un servicio de buena calidad y fiabilidad a sus estudiantes.
Desde diciembre 2019
Profesor desde diciembre 2019
Violin Classes for Children and Adults: Sound quality and technical abilities
A partir de 92.64 $ /h
I propose violin classes for beginners and intermediate level students. I graduated with honours from the Russian Music School named after M. Glinka. I hold several musical award, the top of which is the prize at the Delphic Games of the Modern Era.
Lugar
Clases en el domicilio del estudiante :
- Alrededor de Ecublens, Suiza
Clase en el domicilio del profesor :
- St-Sulpice VD, centre, Saint-Sulpice, Switzerland
Acerca de mí
My name is Anna, I am a postdoctoral researcher in the field of Operations Research & Operations Management, focusing on theory and data-driven applications in the field of multi-stage decision-making under uncertainty. My professional background and practical skills are in the areas of operations research and statistics.
I earned my Ph.D. degree in Statistics and Operations Research at the University of Vienna in April 2014 with a dissertation devoted to theoretical and practical aspects of multi-stage stochastic programming, where the numerical solution of the optimization problem needed to be estimated by approximation of continuous-state stochastic processes with scenario trees. Before I joined the University of Vienna, I received my B.Sc. (2009) and M.Sc. (2011) degrees in Applied Mathematics and Physics at the Moscow Institute of Physics and Technology (National Research University).
I earned my Ph.D. degree in Statistics and Operations Research at the University of Vienna in April 2014 with a dissertation devoted to theoretical and practical aspects of multi-stage stochastic programming, where the numerical solution of the optimization problem needed to be estimated by approximation of continuous-state stochastic processes with scenario trees. Before I joined the University of Vienna, I received my B.Sc. (2009) and M.Sc. (2011) degrees in Applied Mathematics and Physics at the Moscow Institute of Physics and Technology (National Research University).
Formación
1) Ph.D. in Statistics and Operations Research (University of Vienna, Austria).
1) MSc in Applied Mathematics and Physics (Moscow Institute of Physics and Technology, Russia).
2) BSc in Applied Mathematics and Physics (Moscow Institute of Physics and Technology, Russia).
1) MSc in Applied Mathematics and Physics (Moscow Institute of Physics and Technology, Russia).
2) BSc in Applied Mathematics and Physics (Moscow Institute of Physics and Technology, Russia).
Experiencia / Calificaciones
Machine Learning:
University course “Classification, Clustering, and Discrimination” for Master students: The course presents numerical methods used in the areas of classification, clustering, and discrimination. Rather than on classical statistical procedures, the focus is on modern techniques of machine learning which also enable applications to “big data” and business analytics. In particular, supervised and unsupervised learning algorithms are discussed, decision tree techniques are introduced, neural network methodology is outlined, and diverse clustering algorithms are presented.
Statistics:
Multiple university courses for Master students:
Recent courses are focusing on statistical methods for modeling non-linear dependencies in data. Copula models are introduced as a probabilistic way to derive general multivariate distributions based on marginal distributions for each of the random variables and on the information about the underlying dependence structure. Though it is often straightforward to fit marginals to the data, the estimation of a copula function requires complex up-scaling techniques, especially in high-dimensional cases. We introduce vine copulas as a way to take a different strength of interdependencies between random components into account and, further, we introduce hierarchical and ordered copulas as a way to approximate vine structure in very high-dimensional cases. Case studies in risk-management of extremes and in finance are presented.
Mathematics: Multiple university courses for Bachelor and Master students.
Software(MatLab):
The course presents efficient techniques for programming in MatLab. The software can be used for different kinds of "big data" applications. In particular, econometric and financial applications are considered.
University course “Classification, Clustering, and Discrimination” for Master students: The course presents numerical methods used in the areas of classification, clustering, and discrimination. Rather than on classical statistical procedures, the focus is on modern techniques of machine learning which also enable applications to “big data” and business analytics. In particular, supervised and unsupervised learning algorithms are discussed, decision tree techniques are introduced, neural network methodology is outlined, and diverse clustering algorithms are presented.
Statistics:
Multiple university courses for Master students:
Recent courses are focusing on statistical methods for modeling non-linear dependencies in data. Copula models are introduced as a probabilistic way to derive general multivariate distributions based on marginal distributions for each of the random variables and on the information about the underlying dependence structure. Though it is often straightforward to fit marginals to the data, the estimation of a copula function requires complex up-scaling techniques, especially in high-dimensional cases. We introduce vine copulas as a way to take a different strength of interdependencies between random components into account and, further, we introduce hierarchical and ordered copulas as a way to approximate vine structure in very high-dimensional cases. Case studies in risk-management of extremes and in finance are presented.
Mathematics: Multiple university courses for Bachelor and Master students.
Software(MatLab):
The course presents efficient techniques for programming in MatLab. The software can be used for different kinds of "big data" applications. In particular, econometric and financial applications are considered.
Edad
Niños de preescolar (4-6 años)
Niños (7-12 años)
Adolescentes (13-17 años)
Adultos (18-64 años)
Nivel del estudiante
Principiante
Intermedio
Duración
30 minutos
45 minutos
60 minutos
90 minutos
120 minutos
La clase se imparte en
inglés
ruso
alemán
Habilidades
Disponibilidad en una semana típica.
(GMT -05:00)
Nueva York
Clases en el domicilio del profesor
Clases en el domicilio del estudiante
Mon
Tue
Wed
Thu
Fri
Sat
Sun
00-04
04-08
08-12
12-16
16-20
20-24
I am a postdoctoral researcher in the area of operations research and I propose the following classes:
The key to success in mathematics is to learn how to think outside-the-box. This valuable ability helps mathematicians to solve real problems in today’s world. This course helps to develop that crucial ability and, thus, to understand mathematics.
The beginner and intermediate classes focus on the overall understanding of school mathematics and, particularly, of the topics in
1) Algebra
2) Geometry
3) Introduction to probability
The advanced classes focus on particular fields in mathematics taught at universities (e.g., linear algebra, probability theory, optimisation etc.). I also train for the mathematics section of the GMAT exam.
The key to success in mathematics is to learn how to think outside-the-box. This valuable ability helps mathematicians to solve real problems in today’s world. This course helps to develop that crucial ability and, thus, to understand mathematics.
The beginner and intermediate classes focus on the overall understanding of school mathematics and, particularly, of the topics in
1) Algebra
2) Geometry
3) Introduction to probability
The advanced classes focus on particular fields in mathematics taught at universities (e.g., linear algebra, probability theory, optimisation etc.). I also train for the mathematics section of the GMAT exam.
MATLAB is a programming and numeric computing platform used by millions of engineers and scientists to analyze data, develop algorithms, and create models.
I professionally program in MatLab since 2005 and propose classes for all levels and requirements.
I professionally program in MatLab since 2005 and propose classes for all levels and requirements.
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