| Statuss(Aktīvs) | Izdruka | Arhīvs(0) | Studiju plāns Vecais plāns | Kursu katalogs | Vēsture |
| Course title | Interdisciplinary Computing |
| Course code | MateM001 |
| Credit points (ECTS) | 6 |
| Total Hours in Course | 162 |
| Number of hours for lectures | 24 |
| Number of hours for seminars and practical classes | 24 |
| Number of hours for laboratory classes | 0 |
| Independent study hours | 114 |
| Date of course confirmation | 24/01/2024 |
| Responsible Unit | Institute of Mathematics and Physics |
| Course developers | |
| Dr. math., asoc. prof. Svetlana Atslēga |
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| There is no prerequisite knowledge required for this course | |
| Course abstract | |
| The aim of the study course is to acquire the mathematical knowledge and practical skills for applying math techniques to study different problems related to Information technologies. The study course deals with approximation and interpolation of functions, interpolation polynomials, splines, discrete and continuous distributions, Monte-Carlo simulation, and ordinary differential equations at advanced level. The course provides an understanding of the modern concepts and problems of mathematical modelling. In addition, students will acquire skills in working with appropriate software such as Matlab. | |
| Learning outcomes and their assessment | |
| Upon successful completion of this course:
1. Students are able to manage and demonstrate knowledge and critical understanding of interpolation polynomials, mathematical modelling, discrete and continuous distributions, Monte-Carlo simulation, and ordinary differential equations. Students manage the application of the acquired topics in practical examples related to the specialty of the Information Technologies and related fields - Tests 2. Students are able to show understanding of the corresponding concept and regularities, to perform necessary calculations and operations. Working in a group or doing work independently, student is able to apply the mathematical calculations corresponding to the specialty problem situation, to make a professional assessment and interpretation of the intermediate result of the calculations and the final results – independent works |
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| Course Content(Calendar) | |
| 1.Review of software application necessary features according to the content of study course (4 h)
2.Mathematical modelling. Classification of mathematical models (2 h) 3.Models based on ordinary differential equations (2 h) 4.Ordinary differential equations. Solving of ordinary differential equations using appropriate software programs (4 h) 5.Systems of differential equation. Analysis of the phase plane. Applications of system of differential equations in various fields of human activity (7 h) 6.Test 1: Ordinary differential equations (3 h) 7.Approximation of functions. Interpolation. Parabolic interpolation (2 h) 8.Interpolation polynomials (6 h) 9.Mathematical modelling in nature: mathematical modelling of forest stands (2 h) 10.Random variables. Discrete and continuous distributions. Random variables and simulations (3 h) 11.Computer simulation of random processes. Imitation Monte-Carlo methods. (10 h) 12.Test 2: Interpolation polynomials. Discrete and continuous distributions. Monte-Carlo methods (3 h) |
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| Requirements for awarding credit points | |
| The course is assessed through an examination | |
| Description of the organization and tasks of students’ independent work | |
| The following independent works must be completed in writing form or using the appropriate application software:
Independent work 1. Review of software application Independent work 2. Ordinary differential equations and their solvers Independent work 3. Phase plane analysis and construction Independent work 4. Interpolation polynomials Independent work 5. Random variables. Monte-Carlo methods |
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| Criteria for Evaluating Learning Outcomes | |
| The student can receive the accumulative exam score if
-all independent works are defended successfully until the beginning of period of individual studies and examinations -during the semester each test score is at least 4. The mark of the accumulative exam consists of the average mark of all tests. Failed tests can be repeated during the study process at the time indicated by the academic staff. The student can repeat the last test in the 1st week of period of individual studies and examinations at the time indicated by the academic staff. The exam can be arranged at the time indicated by the academic staff if all independent works are successfully defended. |
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| Compulsory reading | |
| 1.Kronbergs E., Rivža P., Bože Dz. Augstākā matemātika II daļa. Rīga: Zvaigzne, 1988. – 527 lpp.
2.Dilwyn Edwards. Guide to mathematical modelling. New York: Industial Press, c2007. x, 326 lpp. 3.Won Y. Yang. Engineering mathematics with MATLAB. 2018. xiv, 741 lpp. |
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| Further reading | |
| 1.Khyruddin Akbar Ansari, Bonni Dichone. An Introduction to numerical methods using MATLAB. SDC Publications. 2019, 368 lpp.
2.Eric Stade, Elisabeth Stade. Calculus: A modeling and computational thinking approach. Springer Cham, 2023. Xii, 274 lpp. 3.Paul Blachard. Differential equations. Belmont: Thomson Brooks/Cole, c2006. xviii, 828 lpp. |
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| Notes | |
| Academic study programme “Information Technologies” | |