Applied Mathematical Stochastic Methods

Course abbrevitation: P_AMSMN
Course code: N0541A170030
Course duration: 2 years

Degree Course Characteristics

The Continuation Master Course in Applied Mathematical Stochastic Methods lays stress on mathematical stochastic methods and their practical applications. It is oriented towards building up an advanced theoretical apparatus of mathematical statistics and other fields of mathematics and towards analysing practical issues the solution of which enables effective application of the theory studied. Students are also trained to apply mathematical statisctics and mathematics to natural sciences or engineering and commercial practice.

The programme is a follow-up to the Bachelor´s Degree Course.

The master programme covers courses on the theory of informatics, regression analysis, generalized linear models, and in specialized disciplines of mathematics and statistics. The theory taught is only a background to practical issues to be solved by effective application of the theory. In practice, the mathematical apparatus designed is to be used for processing and evaluating general statistical data, estimating statistical characteristics based on sampling, and also applied to advanced methods of interactions in particle systems, statistical prediction of defects in materials, control and modelling of traffic flow, and evaluation methodology for statistical data on reliability and extreme events, and data from particle accelerators. The courses provide a deeper insight into the areas concerned and, therefore, will provide a sufficient overview of the present state of the issues.

Part of the degree course is an individual project on an given topic assigned to each student. Thanks to the project, students will have a deeper insight into the whole issue and often arrive at unique results publishable in professional periodicals.

A complex knowledge of modern mathematics, stochastics, physics, and informatics is a good start position for an even more advanced degree and for jobs using this body of knowledge, and mathematical methods in particular, for being successful in research or commerce.

Graduate´s Profile

Knowledge:
Graduates will possess a wealth of knowledge of advanced mathematics and statistics. Based on graduate´s orientation, this knowledge can be used in applied stochastic systems, machine learning, modelling car traffic, data science, digital image processing, financial and actuarial mathematics, modelling pedestrian flow, reliability of component systems, and defectoscopy.

Skills:
The graduate will be skilled in applying advanced methods of mathematics and statistics (or mathematics in general) to tackling existing research, engineering, and commercial issues arising in management optimization, image processing, decision-making under uncertainties, traffic flow simulations, financial mathematics, and dynamic pricing; they will also acquire skills in comparing the outputs of mathematical methods with real empirical/experimental methods. Graduates will also be able to follow new trends in the respective field and be quickly oriented in interdisciplinary issues, to analyse facts and synthesise results. The newly acquired skills will also include a sense of responsibility for the work done and decisions made.

Competency:
With respect to their analytical work habits and ingrained systematic use of modern computer technology, graduates can be employed in industry, banking, research, as well as in private companies and commerce. They can also prove successful in the commercial and private spheres in designing, implementing, and evaluating applied mathematical methods. Moreover, they are also professionally competent to accept managerial roles or research jobs in institutes of the Czech Academy of Sciences, or in research and development centres of big enterprises as well as other research institutions.

State Final Examination

• Methods of regressive analysis – compulsory part of examinationt
• Information theory and random processes – optional part of examination I
• Machine learning – optional part of examination I
• Reliability and extreme events – optional part of examination II
• Mathematical models for traffic flow – optional part of examination II

Details on the examination and its parts are subject to valid legislation and internal regulations and rules and are available at Study Programmes and Regulations.