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.
- nosilec: Jure Žabkar