| Credits: 3,0 | | Study load hours: 81 | Period: semester 2 (3sp)  |
| Language of instruction: English | | Exam contract: not possible |
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The student needs to have a good mathematical background: differentiation, integration, Taylor series, basic linear algebra.
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I: null-,column, row spaces, rank theorem, eigenvalues and eigenvectors, orthogonal matrices, symmetric positive definite matrices, singular value decomposition (singular values), principal components, best low rank matrix, norms of matrices and vectors, Rayleigh quotient, II: Krylov subspaces, Gram-Schmidt orthogonalization, pseudo-inverse and least squares, Householder reflections, III: Fouries transform (continuous, discrete), shift matrices, convolution, Kronecker product.
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| Compulsory coursebooks (printed by bookshop) |
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Gilbert Strang. Linear Algebra and Learning from Data. Wessesley Cambridge Press, 2019, ISBN: 978-0-692-19638-0, Sections: I.1-12, II.1-2, IV:1-3 |
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Lecture ✔
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Small group session ✔
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Semester 2 (3,00sp)
| Evaluation method | |
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| Written exam | 100 % |
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| Multiple-choice questions | ✔ |
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| Off campus online evaluation/exam | ✔ |
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| For the full evaluation/exam | ✔ |
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Second examination period
| Evaluation second examination opportunity different from first examination opprt | |
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Learning outcomes | EC = learning outcomes DC = partial outcomes BC = evaluation criteria |
Master of Statistics and Data Science
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- EC
| The student can handle scientific quantitative research questions, independently, effectively, creatively, and correctly using state-of-the-art design and analysis methodology and software. | | | - DC
| ... correctly using state-of-the-art analysis methodology. | - EC
| The student can critically appraise methodology and challenge proposals for and reported results of data analysis. | - EC
| The student is able to correctly use the theory, either methodologically or in an application context or both, thus contributing to scientific research within the field of statistical science, data science, or within the field of application. | | | - DC
| The student is able to correctly use the theory in an application context, thus contributing to scientific research within the field of statistical and data science.
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| The student is able to correctly use the theory in an application context, thus contributing to scientific research within the field of application. | - EC
| The student is able to efficiently acquire, store and process data. | | | - DC
| ... selecting and using the best data management options | - EC
| The student is capable of acquiring new knowledge. | - EC
| The student knows the international nature of the field of statistical science and data science. |
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| Included in these programmes | Tolerance3 |
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Y
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1 Education, Examination and Legal Position Regulations art.12.2, section 2. |
| 2 Education, Examination and Legal Position Regulations art.15.1, section 3. |
3 Education, Examination and Legal Position Regulations art.16.9, section 2.
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