• Course code:63443
  • Credits:5
  • Semester: summer
  • Contents

Prediction: linear regression, logistic regression, LDA/QDA, nearest neighbors, evaluating goodness of fit.

Feature and model selection: cross-validation, bootstrap, filter methods, wrapper methods.

Advanced prediction: basis expansions, splines, regularization, decision trees, generalized additive models, local regression.

Combining models: bagging, boosting, random forests, ensemble learning.

Support Vector Machines: for classification, for regression, optimization, duality, RKHS (reproducing kernel Hilbert spaces).

Neural networks: fitting neural networks, overfitting and other computational challenges.

  • Study programmes
  • Distribution of hours per semester
15
hours
lectures
5
hours
laboratory work
2
hours
tutorials
  • Professor
Assist. Prof. dr. Jure Žabkar
Instructor
Room:R3.54 - Laboratorij LUI
details