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Linear algebra: from idea to formula - free course from Open Education, training 6 weeks, from 6 to 7 hours per week, Date: December 3, 2023.
Miscellaneous / / December 09, 2023
National Research University Higher School of Economics is a research university that carries out its mission through scientific-educational, project, expert-analytical and sociocultural activities based on international scientific and organizational standards.
We recognize ourselves as part of the global academic community; we consider international partnership and involvement in global university interaction to be key elements of our movement forward. As a Russian university, we work for the benefit of Russia and its citizens.
Our university is a team of scientists, staff, graduate students and students who are distinguished by an internal commitment to maintaining high academic standards in their activities. We strive to provide the most favorable conditions for the development of each member of our team.
Our values:
- The pursuit of truth
- Cooperation and interest in each other
- Honesty and openness
- Academic freedom and political neutrality
- Professionalism, self-demandingness and responsibility
- Active public position
Today the Higher School of Economics is:
- 4 CAMPUSES: MOSCOW, ST. PETERSBURG, NIZHNY NOVGOROD, PERM
- ~7000 TEACHERS AND RESEARCHERS
- 50,400+STUDENTS
- 100,800 GRADUATES
A new element of the Russian education system - open online courses - can be transferred to any university. We make this a real practice, expanding the boundaries of education for every student. A full range of courses from leading universities. We are systematically working to create courses for the basic part of all areas of training, ensuring that any university can conveniently and profitably integrate the course into its educational programs
"Open Education" is an educational platform offering massive online courses from leading Russian universities that have joined forces to provide everyone with the opportunity to receive a high-quality higher education education.
Any user can take courses from leading Russian universities completely free of charge and at any time, and students of Russian universities will be able to count their learning results at their university.
Boris Demeshev is a senior lecturer at the Department of Mathematical Economics and Econometrics, Department of Applied Economics. He graduated from the Bachelor's and Master's degrees at the Higher School of Economics in 2003 with a degree in Mathematical Methods for Economic Analysis.
Boris has extensive experience (more than 10 years) in teaching. Teaches econometrics, probability theory and stochastic analysis. He has repeatedly won the “Best Teacher” competition of the Higher School of Economics. He completed internships at the London School of Economics in econometrics and stochastic analysis in finance, at the University of Sobronn-1 in Paris and at the University of Lucca in Italy. c In 2009–2010 he taught mathematical statistics at the Catholic University of Louvain-la-Neuve in Belgium.
Boris created and maintains the blog pokrovka11.wordpress.com, where materials on various subjects of the department are posted, as well as news in the world of programming.
He is well versed in modern computer technologies in general and publishes materials for his seminars (econometrics, probability theory) in the public domain. In his courses, Boris teaches students to use the statistical package R, showing how in reality they can apply the knowledge acquired during their training.
Boris's research interests lie in the areas of data analysis, Bayesian methods, stochastic analysis and econometrics. Boris is currently working on his Ph.D. thesis. Recently, with Dmitry Borzykh, Boris published a problem book on econometrics, where students are offered both theoretical and practical exercises.
Professional interests:
data visualization
Bayesian approach
Education
2003
Master's degree: Higher School of Economics, Faculty: Economics, specialty “Mathematical methods of economic analysis”
2001
Bachelor's degree: Higher School of Economics, Faculty: Economics, specialty "Economics"
Additional education / Advanced training / Internships
Course "Econometrics in R", lecturer D. Fantazzini, September-October 2014, Higher School of Economics
Course "Spatial Econometrics", lecturer A.K. Bera, University of Illinois, USA, June 2-6, 2014, Higher School of Economics
Summer School of the University of Essex, UK, "Hierarchical Models", August 2012
Awards and achievements
July 2010 Winner of the competition of the Educational Innovation Fund of the National Research University Higher School of Economics with a project for a distance learning program on the course “Auction Modeling”.
November 2011 Winner of the competition of the Educational Innovation Fund of the National Research University Higher School of Economics with an original development "Screencast Series on econometric modeling for undergraduates in non-mathematical and practice-oriented specializations of the Faculty of Economics in the freely distributed cross-platform econometric package Gretl" (co-authored with Vakulenko E.S. and Ratnikova T.A.).
Medal "Recognition - 15 years of successful work" National Research University Higher School of Economics (January 2018)
Gratitude from the Higher School of Economics (November 2013)
Gratitude from the Higher School of Economics (December 2012)
Best teacher – 2019, 2018, 2017, 2016, 2015, 2014, 2013, 2012, 2011
Academic work allowance (2017-2018, 2016-2017, 2015-2016)
1. Vectors and actions with them
In the first chapter we will get acquainted with vectors and learn what a linear operator is, we will learn to invert and transpose some operators. And at the end of the lecture, eigenvectors and eigenvalues will appear on the stage.
In the second chapter, we will learn how to write any linear operator using a table of numbers, invented a way to multiply tables of numbers, and systematize the method of solving a system of equations into the Gaussian algorithm.
3. Matrix determinant and inverse matrix
In the third chapter we will learn to define matrices that calculate areas and volumes. You will have to find the inverse matrix in several ways.
4. Spectral decomposition
In Chapter 4, you will learn how to find eigenvalues and eigenvectors from a matrix. Using this knowledge, we will learn to represent a square matrix as the product of three simpler matrices and master projection to make predictions.
In the penultimate fifth chapter we will see pictures of quadratic forms, and also learn how to determine the set of values of a quadratic form, which is called sign definiteness.
6. Singular value decomposition and principal component method
In the last sixth chapter, we will learn the magic of the SVD decomposition of any matrix into the product of three simple ones, and we will comprehend the statistical interpretation of the decomposition - the method of principal components.
Mechanics
The general course “Mechanics” is part of the general physics course. Students will become familiar with the basic mechanical phenomena and methods of their theoretical description. The lectures include video recordings of physical demonstrations of the mechanical phenomena being studied. Building a course...
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