**Classification methods**: decision trees, Bayesian approach, discriminative functions, LDA, logistic regression, multilayer perceptrone (backpropagation), kernel methods, neural networks on radial-basis functions, nearest neighbours (e.g. condensed), model ensembles (bagging, boosting, cascading).

**Regression methods**: regularised linear regression, polynomial regression, kernel-based linear regression, regression trees.

**Dimensionality reduction**: LDA, PCA, factor analysis and structural equations modelling, latent trait modelling, wavelet transforms.

**Signal processing**: Fourier transform, wavelet transforms, digital filters, denoising, decomposition and forecasting (Holt-Winters model).

**Image processing**: simple pattern-matching techniques, connected components labeling algorithm, familiarity with complex methods such as Hough transform and Viola-Jones method.

**Cluster analysis** of data with vector or tensor structure (signals, surfaces).

**Feature extraction and selection**: filtering and weighting approaches, PCA.

**Recommender Systems**: basic K-NN based on Pearson correlation; rating matrix factorisation using gradient descent.

**Model selection and validation**: K-fold cross-validation, bootstrap techniques, Monte-Carlo techniques, learning curves, manual error analysis.

**Algorithm performance analysis**: R code profiling, time and memory consumption simple analysis techniques.