Linear Algebra and Applications in Data Science DL (9857)
Coordinating lecturer:Prof. dr. Fred VERMOLEN 
Co-lecturer:Prof. dr. Jochen SCHÜTZ 

Credits: 3,0
Study load hours: 81
Period: semester 2 (3sp)

Language of instruction: English
Exam contract: not possible

2nd Chance Exam1: Yes
Final grade2: Numerical
Tolerance3: See included in these programmes
Sequentiality
No sequentiality


Prerequisites

The student needs to have a good mathematical background: differentiation, integration, Taylor series, basic linear algebra.


Content

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.


Compulsory coursebooks (printed by bookshop)
 

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


Organisational and teaching methods
Organisational methods  
Lecture ✔  
Small group session ✔  


Evaluation

Semester 2 (3,00sp)

Evaluation method
Written exam100 %
Closed-book ✔
Multiple-choice questions✔
Off campus online evaluation/exam✔
For the full evaluation/exam✔
Explanation (English)Yes

Second examination period

Evaluation second examination opportunity different from first examination opprt
No


Learning outcomes
  EC = learning outcomes      DC = partial outcomes      BC = evaluation criteria  
Master of Statistics and Data Science
  •  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.

     
  •  DC 

    • 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.

 
Included in these programmesTolerance3
1st year Master Statistics - Bioinformatics DL option mathematics compulsory Y
1st year Master Statistics - Data Science DL option mathematics compulsory Y
1st year Master Statistics - Epidemiology DL option mathematics compulsory Y
1st year Master Statistics Biostatistics DL option mathematics compulsory Y



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.