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Regular version of the site
Master 2019/2020

Complex Calculations Programming

Area of studies: Applied Mathematics
When: 1 year, 1, 2 module
Mode of studies: distance learning
Instructors: Evgeni Burovski
Master’s programme: Control Systems and Data Processing in Engineering
Language: English
ECTS credits: 4

Course Syllabus

Abstract

Numerical computing is an integral part of modern-day scientific research, data analysis and engineering. The art of scientific computing - and the skill of an engineer, researcher or analyst - is a blend of understanding the basic principles of numerical computing, the knowledge of specialized libraries that package individual computational routines with their strengths and limitations, and the ability to mix-and-match these computational primitives into a coherent computational systems that solve business domain problems. This course belongs to the group of adaptation courses, and is designed to bring students up to speed with numerical methods and scientific computing at the level required for further study at the master's level.
Learning Objectives

Learning Objectives

  • The main objective for the course is the development of students' skills of designing and implementing computational models with Python programming language or other high level programming language of the student's choice, and using contemporary development tools.
Expected Learning Outcomes

Expected Learning Outcomes

  • Upon successful completion of the course, a student will be able to •Demonstrate the ability to analyse information and synthesise mathematical models. •Demonstrate the ability of self-directed learning. •Demonstrate the ability to develop non-trivial computational algorithms based on specialized literature and implement them in software. •Use modern development environments, tools and software packages. •Independently develop computational models.
Course Contents

Course Contents

  • The subject of numerical analysis. Building computational pipelines.
  • Finite difference schemes.
  • Numerical linear algebra
  • Integration of functions. Methods of solving integral equations.
  • Finite difference schemes for solving ordinary differential equations.
  • Building compiled extensions for Python programming language.
  • Stochastic modelling algorithms.
Assessment Elements

Assessment Elements

  • non-blocking экзамен
  • non-blocking домашние задания
  • non-blocking Контрольно-измерительные материалы
Interim Assessment

Interim Assessment

  • Interim assessment (2 module)
    0.5 * домашние задания + 0.5 * экзамен
Bibliography

Bibliography

Recommended Core Bibliography

  • Программирование на PYTHON. Т. 1: ., Лутц М., 2013

Recommended Additional Bibliography

  • Linear algebra : concepts and methods, Anthony M., Harvey M., 2012