MPE_MATL MATLAB

Faculty of Economics and Administration
Spring 2023
Extent and Intensity
0/2/0. 3 credit(s). Type of Completion: zk (examination).
Teacher(s)
Mgr. Jakub Chalmovianský, Ph.D. (seminar tutor)
doc. Ing. Daniel Němec, Ph.D. (seminar tutor)
Guaranteed by
Mgr. Jakub Chalmovianský, Ph.D.
Department of Economics – Faculty of Economics and Administration
Contact Person: Mgr. Jarmila Šveňhová
Supplier department: Department of Economics – Faculty of Economics and Administration
Timetable of Seminar Groups
MPE_MATL/01: Thu 10:00–11:50 VT314, except Thu 30. 3., J. Chalmovianský, D. Němec
Prerequisites (in Czech)
MPE_ZMAT Basics of MATLAB
Course Enrolment Limitations
The course is offered to students of any study field.
The capacity limit for the course is 50 student(s).
Current registration and enrolment status: enrolled: 1/50, only registered: 0/50, only registered with preference (fields directly associated with the programme): 0/50
Course objectives
The course is designed to enhance students‘ capabilities for using advanced and more efficient programming methods in MATLAB. MATLAB is a universal and powerful tool used for modeling dynamic systems, optimization and simulations, algorithmization and execution of computationally demanding tasks, advanced analysis, visualization and presentation of data. Provided examples and presented techniques focus mainly on economic and econometric applications. Still, these tools are universal and easily implemented also in other fields, such as finance or in many technical, natural, and social science applications.
By the end of the course, students should be well prepared for working with many features of MATLAB that includes the knowledge of efficient programming techniques in MATLAB (e.g., by using nested and recursive functions, code optimization and parallelization techniques), advanced MATLAB toolboxes (such as Optimization toolbox, Symbolic Math Toolbox, Statistics and Machine Learning Toolbox), advanced data visualization tools (e.g., Mapping toolbox). Students should also learn how to: make their own GUI in MATLAB; use simulation, optimization, and econometric techniques that MATLAB offers; and interconnect MATLAB with a third-party data/software (such as MS Excel, version control system Git, etc.).
Learning outcomes
The course is designed to provide the students with a working knowledge of advanced methods of programming in MATLAB so that:
• they can create, implement, and execute an appropriate algorithm to solve the assigned non-trivial problem, within the MATLAB framework;
• they are able to visualize and analyze real-life (and possibly high-dimensional) data with advanced techniques;
• they understand more advanced programming techniques, including nested and recursive functions, code optimization and parallelization;
• they are aware of the possibilities of various MATLAB toolboxes, such as Optimization Toolbox, Symbolic Math Toolbox, Statistics and Machine Learning Toolbox, Mapping Toolbox;
• they can use the third-party tools for version control or data transfer in connection with MATLAB, and they know techniques of simulations, optimization, and economic modelling.
Syllabus
  • 1. Introduction to econometrics and time-series analysis in MATLAB.
  • 2. Efficient programming techniques and advanced functions.
  • 3. Introduction to object-oriented programming in MATLAB.
  • 4. Advanced graphics.
  • 5. Selected MATLAB toolboxes and their use.
  • 6. Graphical user interface in MATLAB programs.
  • 7. Simulation and optimization techniques using MATLAB.
  • 8. MATLAB and third-party software.
Literature
    required literature
  • ATTAWAY, Stormy. MATLAB® : a practical introduction to programming and problem solving. Fifth edition. Oxford: Butterworth Heinmann/Elsevier, 2019, xxii, 604. ISBN 9780128154793. info
    recommended literature
  • Majumdar, N., Banerjee, S. MATLAB Graphics and Data Visualization Cookbook. Database: eBook Collection (EBSCOhost). 2012. ISBN 9781849693165.
  • Hahn, B. D., Valentine, D. T. Essential Matlab for Engineers and Scientists. 7th ed. Amsterdam : Academic Press. Database: eBook Collection (EBSCOhost). 2019. ISBN 9780081029985.
  • Paluszek, M., Thomas, S. MATLAB Machine Learning Recipes: A Problem-Solution Approach. New York : APress/Springer. 2019. ISBN 9781484239162.
  • Turk, I. Practical MATLAB: With Modeling, Simulation, and Processing Projects. New York : APress/Springer. 2019. ISBN 9781484252819.
Teaching methods
Seminars held in the computer lab, (group) homework, final individual project.
Assessment methods
The course is evaluated on the basis of regular (group) homework and final individual project
Language of instruction
Czech
Follow-Up Courses
Further Comments
Study Materials
The course is taught annually.
The course is also listed under the following terms Spring 2022, Spring 2024, Spring 2025.
  • Enrolment Statistics (Spring 2023, recent)
  • Permalink: https://is.muni.cz/course/econ/spring2023/MPE_MATL