Ágnes Vathy-Fogarassy, PhD
Prerequirements:
-
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.
Literature:
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.