Data Mining Algorithms

- Linear and polynomial, one and multidimensional regression and optimization: gradient descent and least squares

- Supervised learning (classification): nearest neighbour methods, decision trees, logistic regression, non-linear classification, neural networks, support vector networks, timeseries classification and dynamic time warping

- Advanced classification methods: semi-supervised learning, multi-class classification, multi-task learning, ensemble methods: bagging, boosting, stacking, ensemble of classifiers by Dietterich

- Evaluation of classifiers: cross-validation, bias-variance trade-off

- Clustering: k-means (k-medoid, FurthestFirst), hierarchical clustering, Kleinberg's impossibility theorem, internal and external evaluation, convergence speed

- Principal component analysis, low-rank approximation, collaborative filtering and applications (recommender systems, drug-target prediction)

- Density estimation and anomaly detection

- Frequent itemset mining

- Biomedical data processing (next-generation sequencing, gene expression, biomedical timeseries) and mining

- Additional applications and problems: preprocessing, scaling, overfitting, hyperparameter optimization, imbalanced classification

- Tools: Octave/Matlab, Python, R, Hadoop