Zachodniopomorski Uniwersytet Technologiczny w Szczecinie

Szkoła Doktorska - ZUT Doctoral School
specjalność: CHEMICAL ENGINEERING BLOCK

Sylabus przedmiotu Scientific calculation:

Informacje podstawowe

Kierunek studiów ZUT Doctoral School
Forma studiów studia stacjonarne Poziom
Stopnień naukowy absolwenta doktor
Obszary studiów charakterystyki PRK
Profil
Moduł
Przedmiot Scientific calculation
Specjalność przedmiot wspólny
Jednostka prowadząca Katedra Metod Sztucznej Inteligencji i Matematyki Stosowanej
Nauczyciel odpowiedzialny Marcin Korzeń <Marcin.Korzen@zut.edu.pl>
Inni nauczyciele
ECTS (planowane) 1,5 ECTS (formy) 1,5
Forma zaliczenia zaliczenie Język angielski
Blok obieralny Grupa obieralna

Formy dydaktyczne

Forma dydaktycznaKODSemestrGodzinyECTSWagaZaliczenie
wykładyW2 6 0,50,20zaliczenie
laboratoriaL2 10 1,00,80zaliczenie

Wymagania wstępne

KODWymaganie wstępne
W-1Podstawy matemtyki (algebra liniowa oraz rachuek różniczkowy i całkowy)
W-2Podstawowa wiedza i umiejętniości z zakresu programowania.

Cele przedmiotu

KODCel modułu/przedmiotu
C-1Presentation of selected computing toolbox (Matlab, Python, R) for scientific computing
C-2Presentation of selected numerical methods and analytical methods for solving selected differential equations.

Treści programowe z podziałem na formy zajęć

KODTreść programowaGodziny
laboratoria
T-L-1Introduction tocomputing toolboxdo pakietów R, Python Matlab. Data entry (data formats, data frames, pivot tables etc.), matrix2
T-L-2System of linear equations, the eigenvalues, matrix decomposition, types of systems of linear equations, statistical selection procedures2
T-L-3Optimization (linear, quadratic programming, nonlinear programming)1
T-L-4Interpolation and approximation (barycentric interpolation, orthogonal polynomials, statistical plots, Fourier Transform)2
T-L-5Integration, differentiation, solving differential equations, boundary value problem, initial-value problems, Matlab/simulink3
10
wykłady
T-W-1Number representations, IEEE 754 norm, numerical calculation errors, numerical linear algebra, matrix2
T-W-2Selected numerical methods of solving initial problems, interpolation, approximation, optimization2
T-W-3Integration and differential equations2
6

Obciążenie pracą studenta - formy aktywności

KODForma aktywnościGodziny
laboratoria
A-L-1uczestnictwo w zajęciach10
A-L-2Praca wasna, rozwiązywanie zadań19
A-L-3Zaliczenie1
30
wykłady
A-W-1participation in classes6
A-W-2own work8
A-W-3Zaliczenie zajęć1
15

Metody nauczania / narzędzia dydaktyczne

KODMetoda nauczania / narzędzie dydaktyczne
M-1multimedia presentation of the lecture , computer-assisted
M-2solving practical tasks
M-3solving a selected task in accordance with the individually agreed scope

Sposoby oceny

KODSposób oceny
S-1Ocena podsumowująca: classes: practical examination at the computer
S-2Ocena podsumowująca: lecture: test

Zamierzone efekty uczenia się - wiedza

Zamierzone efekty uczenia sięOdniesienie do efektów kształcenia dla dyscyplinyOdniesienie do efektów zdefiniowanych dla obszaru kształceniaCel przedmiotuTreści programoweMetody nauczaniaSposób oceny
ISDE_4-_A02_W01
PhD students have extended knowledge of mathematics in the field of numerical methods used in optimization tasks, computer simulation, linear algebra, interpolation and approximation
ISDE_4-_W02C-1, C-2T-W-1, T-W-2, T-W-3, T-L-1, T-L-2, T-L-3, T-L-4, T-L-5M-1, M-3S-1, S-2

Zamierzone efekty uczenia się - umiejętności

Zamierzone efekty uczenia sięOdniesienie do efektów kształcenia dla dyscyplinyOdniesienie do efektów zdefiniowanych dla obszaru kształceniaCel przedmiotuTreści programoweMetody nauczaniaSposób oceny
ISDE_4-_A02_U01
PhD students can used existing computer tools (R and Python) for data analysis
ISDE_4-_U03, ISDE_4-_U04C-1, C-2T-W-2, T-W-3, T-L-1, T-L-2, T-L-3, T-L-4, T-L-5M-1, M-2S-1, S-2

Zamierzone efekty uczenia się - inne kompetencje społeczne i personalne

Zamierzone efekty uczenia sięOdniesienie do efektów kształcenia dla dyscyplinyOdniesienie do efektów zdefiniowanych dla obszaru kształceniaCel przedmiotuTreści programoweMetody nauczaniaSposób oceny
ISDE_4-_A02_K01
PhD students can critically assess the data obtained within own PhD dissertation using statistical tools
ISDE_4-_K01C-1, C-2T-W-2, T-W-3, T-L-1, T-L-2, T-L-3, T-L-4, T-L-5M-1, M-3, M-2S-1, S-2

Kryterium oceny - wiedza

Efekt uczenia sięOcenaKryterium oceny
ISDE_4-_A02_W01
PhD students have extended knowledge of mathematics in the field of numerical methods used in optimization tasks, computer simulation, linear algebra, interpolation and approximation
2,0
3,0The knowledge acquired during the course are verified on the basis of the developed projects
3,5
4,0
4,5
5,0

Kryterium oceny - umiejętności

Efekt uczenia sięOcenaKryterium oceny
ISDE_4-_A02_U01
PhD students can used existing computer tools (R and Python) for data analysis
2,0
3,0The skills acquired during the tutorials are verified on the basis of the developed projects
3,5
4,0
4,5
5,0

Kryterium oceny - inne kompetencje społeczne i personalne

Efekt uczenia sięOcenaKryterium oceny
ISDE_4-_A02_K01
PhD students can critically assess the data obtained within own PhD dissertation using statistical tools
2,0
3,0The competences acquired during the course are verified on the basis of the developed projects
3,5
4,0
4,5
5,0

Literatura podstawowa

  1. L. N. Trefethen, D. Bau, Numerical linear algebra, SIAM, 1997
  2. Michael J. Crawley, The R Book, Wiley, 2012, 2

Treści programowe - laboratoria

KODTreść programowaGodziny
T-L-1Introduction tocomputing toolboxdo pakietów R, Python Matlab. Data entry (data formats, data frames, pivot tables etc.), matrix2
T-L-2System of linear equations, the eigenvalues, matrix decomposition, types of systems of linear equations, statistical selection procedures2
T-L-3Optimization (linear, quadratic programming, nonlinear programming)1
T-L-4Interpolation and approximation (barycentric interpolation, orthogonal polynomials, statistical plots, Fourier Transform)2
T-L-5Integration, differentiation, solving differential equations, boundary value problem, initial-value problems, Matlab/simulink3
10

Treści programowe - wykłady

KODTreść programowaGodziny
T-W-1Number representations, IEEE 754 norm, numerical calculation errors, numerical linear algebra, matrix2
T-W-2Selected numerical methods of solving initial problems, interpolation, approximation, optimization2
T-W-3Integration and differential equations2
6

Formy aktywności - laboratoria

KODForma aktywnościGodziny
A-L-1uczestnictwo w zajęciach10
A-L-2Praca wasna, rozwiązywanie zadań19
A-L-3Zaliczenie1
30
(*) 1 punkt ECTS, odpowiada około 30 godzinom aktywności studenta

Formy aktywności - wykłady

KODForma aktywnościGodziny
A-W-1participation in classes6
A-W-2own work8
A-W-3Zaliczenie zajęć1
15
(*) 1 punkt ECTS, odpowiada około 30 godzinom aktywności studenta
PoleKODZnaczenie kodu
Zamierzone efekty uczenia sięISDE_4-_A02_W01PhD students have extended knowledge of mathematics in the field of numerical methods used in optimization tasks, computer simulation, linear algebra, interpolation and approximation
Odniesienie do efektów kształcenia dla dyscyplinyISDE_4-_W02They have extended, theory-based knowledge relating to the represented field and discipline and detailed knowledge at an advanced level in the area of scientific research ,methodology of scientific work, preparation of publications and presentations of research results and the principle of dissemination of the results of scientific work, including open access mode.
Cel przedmiotuC-1Presentation of selected computing toolbox (Matlab, Python, R) for scientific computing
C-2Presentation of selected numerical methods and analytical methods for solving selected differential equations.
Treści programoweT-W-1Number representations, IEEE 754 norm, numerical calculation errors, numerical linear algebra, matrix
T-W-2Selected numerical methods of solving initial problems, interpolation, approximation, optimization
T-W-3Integration and differential equations
T-L-1Introduction tocomputing toolboxdo pakietów R, Python Matlab. Data entry (data formats, data frames, pivot tables etc.), matrix
T-L-2System of linear equations, the eigenvalues, matrix decomposition, types of systems of linear equations, statistical selection procedures
T-L-3Optimization (linear, quadratic programming, nonlinear programming)
T-L-4Interpolation and approximation (barycentric interpolation, orthogonal polynomials, statistical plots, Fourier Transform)
T-L-5Integration, differentiation, solving differential equations, boundary value problem, initial-value problems, Matlab/simulink
Metody nauczaniaM-1multimedia presentation of the lecture , computer-assisted
M-3solving a selected task in accordance with the individually agreed scope
Sposób ocenyS-1Ocena podsumowująca: classes: practical examination at the computer
S-2Ocena podsumowująca: lecture: test
Kryteria ocenyOcenaKryterium oceny
2,0
3,0The knowledge acquired during the course are verified on the basis of the developed projects
3,5
4,0
4,5
5,0
PoleKODZnaczenie kodu
Zamierzone efekty uczenia sięISDE_4-_A02_U01PhD students can used existing computer tools (R and Python) for data analysis
Odniesienie do efektów kształcenia dla dyscyplinyISDE_4-_U03They can plan and organise their own and other people’s development and activities with the use of modern methods and tools, especially in the achievements related to the represented field or scientific discipline.
ISDE_4-_U04They can use their knowledge to identify, formulate and solve complex problems in an innovative way in the represented field or discipline by:- defining the purpose and object of research and formulating research hypotheses,- proposing methods, techniques and research tools for solving the research problem, they can infer, critically analyse and evaluate research results and transfer them to the economic and social spheres.
Cel przedmiotuC-1Presentation of selected computing toolbox (Matlab, Python, R) for scientific computing
C-2Presentation of selected numerical methods and analytical methods for solving selected differential equations.
Treści programoweT-W-2Selected numerical methods of solving initial problems, interpolation, approximation, optimization
T-W-3Integration and differential equations
T-L-1Introduction tocomputing toolboxdo pakietów R, Python Matlab. Data entry (data formats, data frames, pivot tables etc.), matrix
T-L-2System of linear equations, the eigenvalues, matrix decomposition, types of systems of linear equations, statistical selection procedures
T-L-3Optimization (linear, quadratic programming, nonlinear programming)
T-L-4Interpolation and approximation (barycentric interpolation, orthogonal polynomials, statistical plots, Fourier Transform)
T-L-5Integration, differentiation, solving differential equations, boundary value problem, initial-value problems, Matlab/simulink
Metody nauczaniaM-1multimedia presentation of the lecture , computer-assisted
M-2solving practical tasks
Sposób ocenyS-1Ocena podsumowująca: classes: practical examination at the computer
S-2Ocena podsumowująca: lecture: test
Kryteria ocenyOcenaKryterium oceny
2,0
3,0The skills acquired during the tutorials are verified on the basis of the developed projects
3,5
4,0
4,5
5,0
PoleKODZnaczenie kodu
Zamierzone efekty uczenia sięISDE_4-_A02_K01PhD students can critically assess the data obtained within own PhD dissertation using statistical tools
Odniesienie do efektów kształcenia dla dyscyplinyISDE_4-_K01They understand the necessity and are prepared to critically analyse the achieved scientific output and the contribution of the results of their own research activity to the development of the represented field and discipline.
Cel przedmiotuC-1Presentation of selected computing toolbox (Matlab, Python, R) for scientific computing
C-2Presentation of selected numerical methods and analytical methods for solving selected differential equations.
Treści programoweT-W-2Selected numerical methods of solving initial problems, interpolation, approximation, optimization
T-W-3Integration and differential equations
T-L-1Introduction tocomputing toolboxdo pakietów R, Python Matlab. Data entry (data formats, data frames, pivot tables etc.), matrix
T-L-2System of linear equations, the eigenvalues, matrix decomposition, types of systems of linear equations, statistical selection procedures
T-L-3Optimization (linear, quadratic programming, nonlinear programming)
T-L-4Interpolation and approximation (barycentric interpolation, orthogonal polynomials, statistical plots, Fourier Transform)
T-L-5Integration, differentiation, solving differential equations, boundary value problem, initial-value problems, Matlab/simulink
Metody nauczaniaM-1multimedia presentation of the lecture , computer-assisted
M-3solving a selected task in accordance with the individually agreed scope
M-2solving practical tasks
Sposób ocenyS-1Ocena podsumowująca: classes: practical examination at the computer
S-2Ocena podsumowująca: lecture: test
Kryteria ocenyOcenaKryterium oceny
2,0
3,0The competences acquired during the course are verified on the basis of the developed projects
3,5
4,0
4,5
5,0