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Course info
KEV / AVM
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Course description
Department/Unit / Abbreviation
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KEV
/
AVM
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Academic Year
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2024/2025
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Academic Year
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2024/2025
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Title
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Application of Computational Methods
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Form of course completion
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Exam
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Form of course completion
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Exam
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Accredited / Credits
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Yes,
4
Cred.
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Type of completion
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Combined
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Type of completion
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Combined
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Time requirements
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Lecture
2
[Hours/Week]
Tutorial
2
[Hours/Week]
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Course credit prior to examination
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Yes
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Course credit prior to examination
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Yes
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Automatic acceptance of credit before examination
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Yes in the case of a previous evaluation 4 nebo nic.
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Included in study average
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YES
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Language of instruction
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Czech, English
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Occ/max
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Automatic acceptance of credit before examination
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Yes in the case of a previous evaluation 4 nebo nic.
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Summer semester
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125 / -
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0 / -
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0 / -
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Included in study average
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YES
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Winter semester
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0 / -
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0 / -
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0 / -
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Repeated registration
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NO
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Repeated registration
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NO
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Timetable
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Yes
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Semester taught
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Summer semester
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Semester taught
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Summer semester
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Minimum (B + C) students
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10
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Optional course |
Yes
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Optional course
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Yes
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Language of instruction
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Czech, English
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Internship duration
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0
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No. of hours of on-premise lessons |
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Evaluation scale |
1|2|3|4 |
Periodicity |
každý rok
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Evaluation scale for credit before examination |
S|N |
Periodicita upřesnění |
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Fundamental theoretical course |
No
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Fundamental course |
No
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Fundamental theoretical course |
No
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Evaluation scale |
1|2|3|4 |
Evaluation scale for credit before examination |
S|N |
Substituted course
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None
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Preclusive courses
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N/A
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Prerequisite courses
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N/A
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Informally recommended courses
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N/A
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Courses depending on this Course
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N/A
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Histogram of students' grades over the years:
Graphic PNG
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XLS
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Course objectives:
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Students master basic mathematical modeling approaches and can apply them to simple tasks in the field of electrical engineering.
Students master the fundamentals of numerical mathematics and know the essential methods of numerical solution of mathematical models.
Students can work with measured datasets and have an overview of mathematical fundamentals of data processing methods.
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Requirements on student
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master the basics of Matlab environment and programming language
work with fundamental programming constructs (cycle, condition, array, function, input / output variable) and algorithms (general iteration, bisection, sorting)
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Content
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1. Introduction to numerical mathematics
2. Linear dynamic systems, ordinary differential equations
3. Numerical methods of solving ordinary differential equations
4. Systems of linear equations
5. Introduction to partial differential equations
6. Numerical methods of solving partial differential equations
7. Interpolation, approximation, search for roots of non-linear equations
8. Normal distribution, examples of Gaussian distribution
9. Fundamentals of regression analysis, least squares method
10. Applied probability and statistics, general regression, selection of regressions
11. Downsampling, bootstrap, cross validation
12. Single and multicriterial optimisation, convex optimisation
13. (reserved, or continuation of optimisation)
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Activities
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Fields of study
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Studentům je k dispozici kurz v Moodle se všemi podstatnými informacemi a materiály.
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Guarantors and lecturers
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-
Guarantors:
Dr. Ing. Jan Přikryl ,
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Lecturer:
Doc. Ing. František Mach, Ph.D. (8%),
Doc. Ing. Karel Noháč, Ph.D. (100%),
Dr. Ing. Jan Přikryl (92%),
Prof. Ing. Václav Šmídl, Ph.D. (100%),
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Tutorial lecturer:
Ing. Martin Janda, Ph.D. (100%),
Ing. Martin Kadlec (100%),
Ing. Pavel Krýsl (100%),
Doc. Ing. František Mach, Ph.D. (20%),
Dr. Ing. Jan Přikryl (20%),
Ing. Jakub Ševčík (20%),
Prof. Ing. Václav Šmídl, Ph.D. (100%),
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Literature
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Time requirements
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All forms of study
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Activities
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Time requirements for activity [h]
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Preparation for an examination (30-60)
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40
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Total
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40
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Combined form of study
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Activities
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Time requirements for activity [h]
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Contact hours
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16
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E-learning [dáno e-learningovým kurzem]
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36
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Individual project (40)
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12
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Total
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64
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Full-time form of study
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Activities
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Time requirements for activity [h]
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Team project (50/number of students)
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12
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Contact hours
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52
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Total
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64
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Prerequisites
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Knowledge - students are expected to possess the following knowledge before the course commences to finish it successfully: |
explain fundamental principles of linear algebra (e.g. vector, linear space and its basis, solving systems of linear equations, eigenvalues) |
explain fundamental terms of calculus (derivative, integral, shape of a graph, rate of change, norm) |
determine derivatives and integrals of fundamental functions |
Skills - students are expected to possess the following skills before the course commences to finish it successfully: |
use the Matlab system as a calculator for matrices and vectors |
write a simple task in the Matlab programming language |
calculate the solution of a system of linear equations |
calculate simple derivatives and integrates of composite functions |
Competences - students are expected to possess the following competences before the course commences to finish it successfully: |
N/A |
N/A |
N/A |
N/A |
N/A |
N/A |
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Learning outcomes
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Knowledge - knowledge resulting from the course: |
explain fundamentals of mathematical modelling of dynamic phenomena |
describe possible sources of errors in numerical computing |
describe methods of approximate numerical solutions to ordinary differential equations |
compile a suitable rule for numerical solution of ordinary or partial differential equations |
explain the principle of optimization and basic optimization methods |
Skills - skills resulting from the course: |
compose a cost function for given optimalisation problem |
apply library methods for solving ordinary differential equations |
perform an iterative calculation of the solution of the partial differential equation by the method of finite differences |
evaluate the quality of regression for given data using cross validation |
recognize the weak regression dependence for given data |
evaluate the quality of a regression model for given input data |
use regression analysis knowledge to gradually improve linear regression models |
Competences - competences resulting from the course: |
N/A |
N/A |
N/A |
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Assessment methods
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Knowledge - knowledge achieved by taking this course are verified by the following means: |
Written exam |
Skills - skills achieved by taking this course are verified by the following means: |
Skills demonstration during practicum |
Seminar work |
Project |
Projekt je skupinový |
Competences - competence achieved by taking this course are verified by the following means: |
Seminar work |
Skills demonstration during practicum |
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Teaching methods
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Knowledge - the following training methods are used to achieve the required knowledge: |
Lecture |
Self-study of literature |
One-to-One tutorial |
Skills - the following training methods are used to achieve the required skills: |
Practicum |
E-learning |
Individual study |
One-to-One tutorial |
Competences - the following training methods are used to achieve the required competences: |
E-learning |
Practicum |
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