Methods of Statistical and Machine Learning (8 ECTS)

Course Code: 
Elective Courses

Supervised and unsupervised statistical learning methods. Determining the type of problem it solves. The concept of distance in Statistics.Clustering (K-means, Hierarchical clustering, Model-based clustering), Classification (LDA, QDA, K-nearest neighbors, Fisher's discriminant analysis). Resampling methods (cross-validation, bootstrap), linear model selection και regularization (subset selection, shrinkage, dimension reduction), multilinear regression, step functions, regression splines,  support vector machines, neural networks.

Recommended Reading

  • Bartholomew D.J., Steele F., Moustaki I., Galbraithe J.I., Ανάλυση Πολυμεταβλητών Τεχνικών στις Κοινωνικές Επιστήμες, Εκδόσεις Κλειδάριθμος ΕΠΕ, 2011.
  • Ιωαννίδης Δ., Αθανασιάδης Ι., Στατιστική και Μηχανική Μάθηση με την R, Εκδόσεις Τζιόλα, 2017.
  • Rajaraman A., Ullman D.J., Εξόρυξη από Μεγάλα Σύνολα Δεδομένων, Εκδόσεις Νέων Τεχνολογιών, 2014.
  • Sidney B., Everitt, Casella G., Fienberg, S., Ingram O., An R and S-PLUS Companion to Multivariate    Analysis, Springer-Verlag London Limited, 2005.
  • Hastie, Tibshirani and Friedman (2009) Elements of Statistical Learning, 2nd edition Springer
  • James, Witten, Hastie and Tibshirani (2011)  Introduction to Statistical Learning with  applications in R, Springer
  • B. S. Everitt, S. Landau, M. Leese, and D. Stahl (2011) Cluster Analysis, Fifth Edition, Wiley

(old title: "Multilinear Statistical Methods")