Advanced Mathematics for PhD Students
BASIC DATA
course listing
A - main register
course code
YMX9301
course title in Estonian
Matemaatika erikursus doktorantidele
course title in English
Advanced Mathematics for PhD Students
course volume CP
-
ECTS credits
9.00
to be declared
yes
assessment form
Examination
teaching semester
spring
language of instruction
Estonian
English
Study programmes that contain the course
Structural units teaching the course
LT - Department of Cybernetics
Timetable link
Version:
VERSION SPECIFIC DATA
course aims in Estonian
Omandada vajalikud baasteadmised matemaatikas doktoriõppe jaoks
course aims in English
To acquire necessary basic knowledge in mathematics for doctoral studies.
learning outcomes in the course in Est.
Aine läbinud üliõpilane
tunneb harilike ja osatuletistega diferentsiaalvõrranditega seotud põhimõisteid ja seoseid ja oskab lahendada vastavaid ülesandeid;
omab teadmisi lineaaralgebrast ja Fourier analüüsist ning oskab neid teadmisi rakendada erialaste probleemide lahendamisel;
tunneb peamisi numbrilisi meetodeid matemaatilise modellerimise ülesannete lahendamisel;
teab kompleksanalüüsi või tõenäosusteooria ja matemaatilise statistika peamisi meetodeid ja oskab neid kasutada erialaste ülesannete lahendamisel.
tunneb harilike ja osatuletistega diferentsiaalvõrranditega seotud põhimõisteid ja seoseid ja oskab lahendada vastavaid ülesandeid;
omab teadmisi lineaaralgebrast ja Fourier analüüsist ning oskab neid teadmisi rakendada erialaste probleemide lahendamisel;
tunneb peamisi numbrilisi meetodeid matemaatilise modellerimise ülesannete lahendamisel;
teab kompleksanalüüsi või tõenäosusteooria ja matemaatilise statistika peamisi meetodeid ja oskab neid kasutada erialaste ülesannete lahendamisel.
learning outcomes in the course in Eng.
Student passing the course
knows main concepts and relations of of ordinary and partial differential equations and is able to solve related problems;
has a knowledge concerning linear algebra and Fourier analysis and is able to apply this knowledge in solved problems of a speciality;
knows main numerical methods to solve problems of mathematical modelling;
knows main methods of complex analysis or probability theory and mathematical statistics and is able to apply this knowledge to solve problems of speciality
knows main concepts and relations of of ordinary and partial differential equations and is able to solve related problems;
has a knowledge concerning linear algebra and Fourier analysis and is able to apply this knowledge in solved problems of a speciality;
knows main numerical methods to solve problems of mathematical modelling;
knows main methods of complex analysis or probability theory and mathematical statistics and is able to apply this knowledge to solve problems of speciality
brief description of the course in Estonian
Harilikud diferentsiaalvõrrandid ja nende süsteemid, Laplace teisendus, maatriksite omaväärtused, mitmemõõtmeline matemaatiline analüüs, Fourier meetod, osatuletistega diferentsiaalvõrrandid, kompleksanalüüs, konformsed teisendused, numbrilised meetodid võrrandite, süsteemide ja diferentsiaalvõrrandite lahendamisel, tõenäosusteooria ja matemaatilise statistika meetodid. Teemade proportsioonid sõltuvad konkreetse tudengi või tudengiterühma erialast.
brief description of the course in English
Ordinary differential equations and their systems, Laplace transform, matrix eigevalue problems, multivariate calculus, Fourier method, partial differential equations, complex analysis, conformal mappings, numerical methods to solve equations, systems and differential equations, methods of probability theory and mathematical statistics. Proportions of themes depend on specialities of students or student groups.
type of assessment in Estonian
eksam
type of assessment in English
exam
independent study in Estonian
-
independent study in English
-
study literature
E. Kreyszig, Advanced engineering mathematics. Wiley, 2006.
study forms and load
daytime study: weekly hours
6.0
session-based study work load (in a semester):
lectures
2.0
lectures
-
practices
0.0
practices
-
exercises
4.0
exercises
-
lecturer in charge
-
type (CBL/PBL)
not specified
LECTURER SYLLABUS INFO
semester of studies
teaching lecturer / unit
language of instruction
Extended syllabus
2026/2027 spring