I got my BSc and MSc in Physics & Mathematics from MIPT and Skoltech, with a specialisation in theoretical/computational physics (integrable and chaotic quantum systems, to be certain). Then I somewhat shifted towards machine learning, working as a data scientist for App in the Air (ask me about recommender systems) for a year, as well as teaching 10-11th grades IB DP Mathematics (passionate about teaching). For now, I’m a PhD student at the CS department of HSE, working on what’s called topological data analysis, also coding some geometry for an architecture startup, Archeads Inc.
Our main goal is to master the concepts: vector spaces, linear dependence/independence, span and rank, matrices and their arithmetic, the basis, orthogonality, determinants, etc. – discovering them ourselves by solving problems (motivated by practice).
SKILLS:
- Algorithms
- Combinatorics
- Data Analysis
- Discrete Optimisation
- Dynamical Systems
DATE: 8 Jun - 26 Jun, 2020
DURATION: 3 Week
LECTURES: 3 Hours per day
LANGUAGE: English
LOCATION: Barcelona, Harbour.Space Campus
COURSE TYPE: Offline
WHAT YOU WILL LEARN
ABOUT MAXIM
Linear algebra – a study of lines, planes, vectors, linear equations, transforms, matrices, tensors – is the heart of many areas of mathematics. It spans from pure (like representation theory), to applied to any natural sciences such as physics & chemistry (linear algebra is the language of quantum mechanics), any numerical modeling (including mechanical and networks engineering) and data processing, statistical inference and machine learning. This course will be the beginning of your journey in this exciting field, covering the required theory and providing you with a lot of practice (including some numerical programming)!
MAXIM BEKETOV
CS PhD student at the HSE
Python developer at Archeads Inc.
3-rd year IB Mathematics Diploma Programme teacher
DATE: 8 Jun - 26 Jun, 2020
DURATION: 3 Weeks
LECTURES: 3 Hours per day
LANGUAGE: English
LOCATION: Barcelona, Harbour.Space Campus
COURSE TYPE: Offline
All rights reserved. 2017
COURSE OUTLINE
Session 1
Lecture: Introduction. Vectors, linear combinations, the dot product, vector’s length
Seminar (Lab): Vectors in Python & NumPy, comparing data with the dot-product
Session 2
Lecture: Systems of linear equations (SLEs)
Seminar: Solving problems on SLEs
Session 3
Lecture: (Gaussian) Elimination: the idea of it, elimination in terms of (augmented) matrix notation for SLEs
Seminar: Solving SLEs with elimination. Commenting on the code implementation of GE
Session 4
Lecture: Matrices and matrix operations, inverse matrices
Seminar: Solving problems on matrix arithmetic
LINEAR ALGEBRA - 1
LINEAR ALGEBRA - 1
BIBLIOGRAPHY
Coding the Matrix: Linear Algebra through Computer Science Applications by Philip Klein (Newtonian Press, 2015)
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