Supervised learning methods:regression,classification, support vector methods, boosting decision trees, random forest, model selection and assessment:feature engineering, cross validation methods.
Unsupervised learning: K-means clustering,spectral methods, EM algorithm. Dimensionality reduction and data visualization techniques, graphical models. Time series analysis. Examples from domain areas like value chains, transport, communication networks and health-care.
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