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Presentation
Presentation
The Numerical Analysis course aims to give students the tools to: recognise the need to use numerical methods and the relevance of the concept of error; know some classical numerical methods of solving systems of linear equations and non-linear equations; know some classical numerical methods of interpolation and approximation and of quadrature; use an appropriate computational system to evaluate the methods; and develop critical thinking, autonomous and group work capacity.
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Class from course
Class from course
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Degree | Semesters | ECTS
Degree | Semesters | ECTS
Bachelor | Semestral | 6
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Year | Nature | Language
Year | Nature | Language
1 | Mandatory | Português
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Code
Code
ULHT6634-335
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Prerequisites and corequisites
Prerequisites and corequisites
Not applicable
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Professional Internship
Professional Internship
Não
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Syllabus
Syllabus
S1. Python for Numerical Analysis S2. Errors and errors propagation S3. Linear Regression S4. Interpolation S5. Non-linear equations S6. Systems of linear equations S7. Numerical integration
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Objectives
Objectives
The main objectives of this unit are: LO1. Understand the finite limitation of the numerical algorithms; LO2. Work with error estimates and understand the error propagation in algorithms; LO3. Solve non-linear equations and systems of linear equations using numerical methods; LO4. Interpolate and extrapolate data using interpolation and minimum square errors. Apply to data science and experimental measurements; LO5. Approximate functions and integrals using numerical methods; LO6. Develop elementary computation projects. Apply to diverse problems of data science.
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Teaching methodologies
Teaching methodologies
There are theoretical and practical classes, being mainly expositives and in-person lectures. TM1. Lectures. TM2. Practical, incorporating both explanatory segments and exercises. TM3. Theoretical and practical exercise assignments. TM4. Independent project development. TM5. Recommendation of supplementary materials.
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References
References
Slides e apontamentos das aulas Pedro M. A. Miranda, Laboratório Numérico (em python). Disponível em: https//fenix.ciencias.ulisboa.pt/downloadFile/2251937252639182/LabNum_2018_v4.pdf Qingkai Kong, Timmy Siauw, Alexandre Bayen, Python Programming and Numerical Methods. A Guide fir Engineers and Scientists. ISBN: 9780128195499. Disponiível em: https://pythonnumericalmethods.berkeley.edu/notebooks/Index.html
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Assessment
Assessment
A avaliação é composta por:
- Avaliação contínua: dois testes realizados em aula.
- Avaliação final, em único teste de 90 minutos, caso o aluno não tire uma nota suficiente na avaliação contínua.
- Recurso, em único teste de 90 minutos.
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Mobility
Mobility
No
