Machine Learning

Machine Learning

Our expertise in Machine Learning is reflected by and related to several lectures, projects, as well as experts.

Medical Imaging is one research branch of GRIS, combining Machine Learning with Deep Learning in the medical field.

Related to the latter is our research on Deep Generative Models

One particular focus of GRIS is on Interactive Machine Learning, following the idea to have the user in the loop in a highly iterative Machine Learning Process.

Data Mining approaches investigated at GRIS often refer to Machine Learning.

Machine Learning explained

Machine Learning uses statistical techniques to learn from data and make predictions on data, e.g., to progressively improve the performance for a specific learning task. In most cases the learning model is not explicitly programmed. Influencing research fields are artificial intelligence, pattern recognition and computational learning theory. Machine Learning techniques can be differentiated into supervised, semi-supervised, and unsupervised techniques, depending on the awareness/availability of a target variable. In the last decade, Deep Learning has gained a lot of intention, a Machine Learning field that emphasizes the optimization of artificial neural networks with several hidden layers between the input and the output of the learning model.