Elements of linear algebra and algorithmics for data analytics

This course is aimed at training participants in conceptual and theoretical elements for addressing machine learning. Particularly, the area of pattern recognition studied from a linear algebra and functional analysis point of view. In addition, some recommendations are given for the typesetting and presentation of scientific articles in this area.

Find full course material here.

Lecture I: Elements of linear algebra
Vector and vector spaces
Notation of vectors and matrices
Euclidean and Hilbert spaces
Linear transformation, inner product, and norm
Base, linear independence, orthogonality, rank and vector space span
Eigenvalues ​​and eigenvectors
Lecture II: Data representation and classification
General scheme of pattern recognition
Outlier, relevance analysis and variable transformation
Feature extraction and selection
Classification and clustering
Optimization of quadratic forms: Application in PCA and SVM
Heuristics: K-means and generic center-based clustering
Lecture III: Equation interpetation and algorithm writing
 Recommendations for writing and interpreting equations
 Recommendations for the formulation of pseudocodes
 Advanced topics of scientific text editors
 Writing and translation of scientific articles: Practical example