Review of linear and nonlinear regression and brief introduction to generalized linear models, the course covers selected methods of dimension reduction (principal components, factor analysis, canonical correlations), of unsupervised (clustering, multidimensional scaling ordination) and supervised classification (discriminant analysis, logistic regression, nearest neighbours - including, among others, the machine learning methods like classification trees, neural networks, and support vector ...
Review of linear and nonlinear regression and brief introduction to generalized linear models, the course covers selected methods of dimension reduction (principal components, factor analysis, canonical correlations), of unsupervised (clustering, multidimensional scaling ordination) and supervised classification (discriminant analysis, logistic regression, nearest neighbours - including, among others, the machine learning methods like classification trees, neural networks, and support vector ...
