Mathematics for data science - Einzelansicht

After the course the students will be familiar with the fundamental concepts of data science. The participants can analyse given data sets with respect to dimensionality reduction and clustering. They also know the basic structure of neural networks and support vector machines to solve classification tasks. The participants know relevant methods from linear algebra and optimization and can apply these techniques. This embraces the design of appropriate algorithms and the implementation of different numerical methods to solve the corresponding problems. 

R.A. Horn, C. R. Johnson Matrix Analysis, Cambridge Univ. Press 2013

D.A Simovici. Mathematical Analysis For Machine Learning And Data Mining-World Scientific, 2018.  M.P. Deisenroth, A. A. Faisal, C. S. Ong, Mathematics for Machine Learning. Cambridge University Press 2021. D.A Simovici. Linear Algebra Tools for Data Mining. World Scientific, 2013. C.C. Aggarwal. Neural networks and deep learning: a textbook. Springer 2018.

linear regression, logistic  regression, dimension reduction, principle component analysis, linear discriminant analysis, multidimensional scaling, k-means clustering, support vector machines, kernel methods, neural networks, natural languange processing

B. Sc.; Analysis and Linear Algebra at Bachelor level, knowledge of Matlab or Python

1 oral exam  
”Mathematics for data science”
 30 min (100%) / SuSe + WiSe