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The talk will first give an oversview of the education and research at the University Mediteranean Podgorica, followed by a technical presentation on Open Source Machine Learning Software for Structured Regression Models. Structured regression models are designed to use relationships between objects for predicting output variables. In other words, structured regression models consider the attributes of - and dependencies between objects to make predictions as accurate as possible. The Gaussian Conditional Random Fields (GCRF) model is a commonly used structured regression model that incorporates the outputs of traditional supervised learning models (unstructured predictors) and the correlation between output variables in order to achieve a higher prediction accuracy. A main assumption in the GCRF model is that if two objects are closely related, they should be more similar to each other and they should have similar values of the output variable. While the similarity considered in GCRF is symmetric, in many real-world examples, however, objects are asymmetrically linked. This approach presents an extension of the GCRF model that considers asymmetric similarities between objects (called Directed GCRF). This talk also presents an open-source software tool that integrates various GCRF methods and supports training and testing of those methods on different datasets using graphical user interface.