Message Passing Interface (MPI) is a standardized and portable message-passing standard designed to function on parallel computing architectures.

As the power and capabilities of computing increases, Artificial Intelligence solutions takes a greater role to perform and execute various processes. Seminar is intended to provide insight into artificial neural networks, give practical examples of deep learning applications and solution implementation using Python and Tensorflow.

Participants will get hands-on experience in implementing deep learning solutions by using Python which currently is one of the most popular programming languages.

As the power and capabilities of computing increases, Artificial Intelligence solutions takes a greater role to perform and execute various processes. Being a part of Artificial Intelligence, Machine Learning provides computer learning and decision-making based on the provided data. Seminar is intended to provide insight into Machine Learning and its algorithms covering supervised and unsupervised learning, including data processing and application for machine learning solutions.

Research generates significant amounts of data that are used to communicate the results of a particular investigation. However, currently, these data are usually unstructured and highly scattered. As a result, this data can not be used to verify, replicate or reanalyze the findings. Moreover, different standards, annotation practices and data formats for data and metadata (if available) might have been used by other researchers, introducing additional heterogeneity and difficulties in later data integration and interpretation.

The traditional approach to research programmes is to assume that students will find a way to analyse and visualise their data. This assumption brings problems for the students, their supervisors and a significant waste of time. Many students are scared by the data rather than curious and usually skip exploratory data analysis and go straight to advanced statistical models that they cannot explain later because they do not understand their data in depth.