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 algebraVector 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 classificationGeneral 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 |