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Ágnes Vathy-Fogarassy, PhD


Topics covered

Students will learn about the following topics, including their individual training plans and interests:

T1. Basic and advanced classification methods:

  • classification methods (e.g. decision trees, Naive Bayes classification, k-nn classification, Linear Discriminant Analysis, binary and multinominal logistic regression, Support Vector Machines, the kernel trick, Maximal Margin Classifier, Support Vector Classifier)
  • embedded methods (bagging, boosting, stacking)

T2. Basic and advanced regression methods:

  • regression methods (linear regression, nonlinear regression, Ridge, Lasso, Elastic Net, orthogonal regression, Decision Tree Regressor, k-nn Regressor)

T3. Evaluation the results:

  • measuring the error in classification 
  • measuring the error in regression
  • model comparison
  • model validation: overfitting, validation techniques

T4: Handling problems of small and large datasets:

  • the curse of dimensionality
  • measuring predictor importance 
  • feature selection, selection bias
  • hyperparameter-tuning

The assessment will be based on the development of an individual project task related to the above topics.


1.      Max Kuhn, Kjell Johnson: Applied Predictive Modelling, Springer (2013), p. 615

2.      Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani: An Introduction to Statistical Learning, Springer (2017), p. 440

Field Cady: The Data Science Handbook, Wiley (2017), Chapter 8.