The course consists of the following contents. Data representation in multidimensional heterogeneous datasets. Dataset cleaning and feature engineering. Application of R and/or Python for machine learning based visualization. Cluster analysis (e.g. k-nearest-neighbor, hierarchical cluster analysis, spectral clustering, naive Bayes, and classification e.g. logistic regression, support vector machines, decision trees, random forests, deep neural networks, recurrent neural networks. Cloud services for big data analysis is also introduced, including tools for administration of a server platform.

To give the students a basic understanding of statistical, datarelated concepts and experience in the use of statistical methods for analysis and datavisualisation of a given set of data. The course consists of the following contents. Stochastic variables and distributions. (Binomial, Poisson, Uniform, Chi-square, Student's t etc.) Estimation of parameters in statistical models, Tests of hypotheses, R program for theoretical support, application, analysis, and visualization.