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.