Higher Mathematics and Operations Research
BASIC DATA
course listing
A - main register
course code
VAY1100
course title in Estonian
Kõrgem matemaatika ja operatsioonianalüüs
course title in English
Higher Mathematics and Operations Research
course volume CP
-
ECTS credits
6.00
to be declared
yes
assessment form
Examination
teaching semester
autumn - spring
language of instruction
Estonian
English
Study programmes that contain the course
code of the study programme version
course compulsory
VDSR25/25
yes
Structural units teaching the course
V - Estonian Maritime Academy
Course description link
Timetable link
View the timetable
Version:
VERSION SPECIFIC DATA
course aims in Estonian
Tutvustada tõenäosusteooria põhilisi mõisteid ja meetodeid. Süvendada teadmisi juhuslikkusest. Anda oskusi juhuslikkuses peituvate seaduspärasuste kirjeldamiseks statistika meetodite abil. Süvendada teadmisi ja oskusi andmetöötluses. Esitada arv- ja funktsionaalridade põhiprobleemide praktilised rakendused. Õpetada lahendama mainitud teooriaga seotud põhilisi ülesandeid. Harjutada üliõpilasi matemaatilise mõtlemise ja sümboolikaga.
Omandada teadmisi operatsioonianalüüsi ülesannetest ja meetoditest. Õppida lahendama lineaarse planeerimise ülesandeid.
course aims in English
To give an overview of the main methods stemming from the theory of probability. To deepen the knowledge about randomness. To give knowledge about the laws of random phenomena and ability to indentify them by means of methods of statistics. To deepen knowledge and skills for data processing. To give knowledge about the theory of functional series and their applications. To teach to solve main problems of the theory mentioned above. To make the students accustomed with the mathematical thinking and symbolism.
To obtain a knowledge about problems and methods of operations research. To learn to solve problems of linear programming
learning outcomes in the course in Est.
Aine läbinud üliõpilane:
• tunneb tõenäosusteooria põhimõisteid, oskab leida sündmuste summa ja korrutise tõenäosust ning kasutada täistõenäosuse, Bayesi ja Bernoulli valemeid ülesannete lahendamisel;
• tunneb juhusliku suuruse, selle jaotusfunktsiooni, jaotustiheduse, karakteristliku funktsiooni, genereeriva funktsiooni ja põhiliste arvkarakteristikute mõisteid ning oskab lahendada ülesandeid enamlevinud jaotuste parameetrite määramise ja nendega seotud tõenäosuste arvutamise kohta;
• tunneb juhusliku vektori, selle jaotusfunktsiooni, jaotustiheduse mõisteid ning oskab lahendada vastavaid ülesandeid;
• tunneb matemaatilise statistika põhimõisteid, oskab leida punkt- ja vahemikhinnanguid;
• oskab kasutada MS Exceli korrelatsioon- ja regressioonananlüüsi vahendeid nähtustevaheliste seoste uurimiseks ja kirjeldamiseks
• uurida astmeridade koonduvust, arendada funktsiooni astmeritta ja kasutada astmeridu rakendustes;
• arendada funktsiooni Fourier' ritta, leida funktsiooni Fourier' teisendust ning kasutada Fourier' ridu ja Fourier' teisendusi;
• testida praktiliste ülesannete lahendamisel saadud tulemuste õigsust.
• tunneb operatsioonianalüüsi põhimõisteid ja -seoseid;
• oskab lahendada laiemaid probleeme, valides häid vahendeid ja kontrollida vastuseid.
• tunneb otsustuste teooria elemente ja oskab lahendada lihtsamaid maatriksmänge.
• oskab formuleerida lihtsamaid lineaarplaneerimise ülesandeid ja lahendab neid graafiliselt ning hariliku või duaalse simpleksmeetodiga.
learning outcomes in the course in Eng.
Having finished the study of the subject a student:
• knows the main concepts of the theory of probability, is able to find probabilities of sums and products of events and use the formula of total probability and Bayes and Bernoulli formulas to solve problems;
• knows the concepts of the random variable, distribution function, density function, characteristic function, generating function and main numerical characteristics and is able to solve problems to find parameters of distributions and to compute related probabilities;
• knows the concepts of event, its distribution function, density and is able to solve related problems;
• knows of main concepts of the mathematical statistics, is able to find point and interval estimators;
• is able to use MS Excel tools of correlation and regression analysis and describe linear relationships between variables
• is able to investigate the convergence of functional series;
• is able to find Fourier-series expansions, Fourier transforms of function and to apply Fourier series and transforms;
• is able to check the correctness of results obtained by solution of practical exercises.
• knows the main concepts and issues of operations research;
• is able to use methods in volume of a subject.
• is able to apply theoretical knowledge into practice
• is know the fundamentals of decision theory and will be able to solve simple matrix games.
• is be able to formulate simple problems of linear programming and solve them graphically and with the help of the ordinary or dual simplex method.
brief description of the course in Estonian
Tõenäosusteooria põhimõisted ja seosed. Juhuslik suurus. Jaotusfunktsioonid. Juhuslikud protsessid. Matemaatilise statistika põhimõisted. Akvakarakteristikud ja vahemikuhinnangud. Dispersioon, korrelatsioon, regressioon. Ms Exceli vahendid andmete töötlemiseks. Astmeread. Fourier' rida. Taylori rida. Fourier' teisendus. Matemaatika tarkvara. Lineaarplaneerimine. Simpleksmeetod ja tema erijuhud. Duaalne simpleksmeetod. Transpodiülesanne. Täisarvuline planeerimine. Mänguteooria elemendid.
brief description of the course in English
Main concepts of the theory of probability. Random variable. Distribution function. Main concepts of the mathematical statistics. Numerical characteristics. Confidence intervals for parameters. Dispersion. Regression. Correlation.
Computer statistics. Power series. Fourier' series. Taylor’ series. Fourier' transforms. Mathematical software.
Standard and canonical forms of linear programming problems. Optimal solutions. Dual linear programming. Duality theorems. Graphical solutions of programming problems. Simplex method. Integer programming. Transportation problems, Elements of game theory.
type of assessment in Estonian
Eristav hindamine
type of assessment in English
Grading
independent study in Estonian
-
independent study in English
-
study literature
.
study forms and load
daytime study: weekly hours
4.0
session-based study work load (in a semester):
lectures
0.0
lectures
-
practices
0.0
practices
-
exercises
4.0
exercises
-
lecturer in charge
-
LECTURER SYLLABUS INFO
semester of studies
teaching lecturer / unit
language of instruction
Extended syllabus
2024/2025 spring
Anna Saksa, LT - Department of Cybernetics
Estonian
    hindamine_VAY1100_ing.pdf 
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    2023/2024 spring
    Anna Saksa, LT - Department of Cybernetics
    Estonian
      hindamine_VAY1100_ing.pdf 
      2022/2023 spring
      Anna Saksa, LT - Department of Cybernetics
      Estonian
        2021/2022 spring
        Anna Saksa, LT - Department of Cybernetics
        Estonian
          hindamine_VAY1100_ing.pdf 
          2021/2022 autumn
          Anna Saksa, LT - Department of Cybernetics
          Estonian
            hindamine_VAY1100_ing.pdf 
            2020/2021 spring
            Anna Saksa, LT - Department of Cybernetics
            Estonian
              hindamine_VAY1100_ing.pdf 
              2020/2021 autumn
              Anna Saksa, LT - Department of Cybernetics
              Estonian
                hindamine_VAY1100_ing.pdf 
                2019/2020 spring
                Anna Saksa, LT - Department of Cybernetics
                Estonian
                  hindamine_VAY1100_ing.pdf 
                  2019/2020 autumn
                  Anna Saksa, LT - Department of Cybernetics
                  Estonian
                    hindamine_VAY1100_ing.pdf 
                    2018/2019 spring
                    Anna Saksa, LT - Department of Cybernetics
                    Estonian
                      hindamine_VAY1100_ing.pdf 
                      2017/2018 spring
                      Anna Saksa, LT - Department of Cybernetics
                      Estonian
                        hindamine_VAY1100_ing.pdf 
                        Course description in Estonian
                        Course description in English