- Matrix Algebra and Extremals of Matrix Functions
- Multivariate Analysis and Random Vectors
- Dimensionality Reduction Methods
- Principal Component Analysis
- Multidimensional Scaling
- Diffusion Map
- Classification and Cluster Analysis
- Linear Discriminant Analysis
- Maximum A-posteriori Probability (MAP) Classifier
- Maximum Likelihood (ML) Classifier
- Spectral Clustering
- Hierarchical Clustering
- Support-Vector Machines
- Convex Optimization Theory
- Support-Vector Machine (SVM) Algorithm
- Sequential Minimal Optimization (SMO) Algorithm
- Kernel SVM
- R. Mathar, G. Alirezaei, E. Balda, and A. Behboodi: Fundamentals of Data Analytics with a View to Machine Learning, Springer Nature Switzerland AG, 2020.
- K. V. Mardia, J. T. Kent, and J. M. Bibby: Multivariate Analysis, Academic Press, 1979.
- A. S. Bandeira: Ten Lectures and Forty-Two Open Problems in the Mathematics of Data Science, Lecture Notes, MIT Open Course Ware, 2015.
- R. O. Duda, P. E. Hart, and D. G. Stork: Pattern Classification, John Wiley & Sons Inc, 2000.
- T. Hastie, R. Tibshirani, and J. H. Friedman: The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer, 2009.
- K. P. Murphy: Machine Learning: A Probabilistic Perspective, MIT Press, 2012.
- J. Leskovec, A. Rajaraman, and J. D. Ullman: Mining of Massive Datasets, Cambridge University Press, 2020.
- C. Aggarwal: Data Mining: The Textbook, Springer, 2015.
- C. D. Meyer: Matrix Analysis and Applied Linear Algebra, Society for Industrial and Applied Mathematics (SIAM), 2000.
- R. A. Horn and C. R. Johnson: Matrix Analysis, Cambridge University Press, 2012.
- R. Mathar: Multidimensionale Skalierung: Mathematische Grundlagen und algorithmische Aspekte, Teubner Verlag, 1997.
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Last edited: 2020-08-26 12:20:21