The students of the module will be able to explain visual perception and processing of visual information using established models, explain the reference model of information visualization and compare it with other models, explain the process of visual exploration and decision-making, and judge by example, visual layouts based on given tasks and deploy data and assess adequate deployment, use visual variables based on data types, and develop interactive visualizations of abstract data.
After attending the event, the students will be familiar with the principles and methods of user-centered development and will be able to assess at what point in the project they should best be used. They understand human visual, motor and cognitive abilities and their relevance to design and can apply them. They can assess interface elements and their suitability for specific problems and are able to design such elements themselves. They know the established methods of prototyping and can apply them for various projects and projects. Furthermore, they know the most common methods for performing qualitative and empirical evaluation methods and are able to design and carry out tests independently.
Artificial intelligence applications are used in very different areas to solve different kinds of problems. Different artificial intelligence approaches are applied, whereby it must be assessed in each case which method is best suited for which problems.
The module aims to acquire methodological, analytical, and technical competencies to use artificial intelligence according to needs and application scenarios. For this purpose, some approaches to machine learning will be discussed methodically first. In particular, knowledge of the distinction between supervised and unsupervised methods and overfitting, regression, and classification will be acquired, which serve as canonical foundations for artificial intelligence methods. Based on this, methods of neural networks, perceptron models, and activation functions will be applied. The module enables the practical application of artificial intelligence methods in different scenarios and the critical analysis of these methods.
The students of the module can explain the differences between information visualization, graphic design and visual analytics, explain the reference models of visual analytics, explain elementary methods of data mining, clustering and information extraction, independently deal with current primary literature and the methods described there for the use of Assess complex analytical tasks, explain the process of visual exploration and decision-making, and give examples.
The students of the module will be able to explain visual perception and processing of visual information using established models, explain the reference model of information visualization and compare it with other models, explain the process of visual exploration and decision making, and give examples, visual layouts for use in the field Use and evaluate the field of visual analysis for future forecasts and independently develop interactive visual forecast analytics systems.
Due to the increasing digitization, analyzes are a constant necessity, which also applies to the identification of errors, problems or opportunities for improvement. With visual analytics, the combination of algorithmic and massive calculation opportunities of computer-based systems on the one side as well as the visual and cognitive skills of the users on the other side are used together. The seminar focuses on the use of visual analytics in the two current areas of smart manufacturing and trend analysis. On behalf of the given task, current approaches, methods and implementations should be researched and examined. The goal should be a scientific elaboration based on empirical and scientific standards.
The aim of the module is the acquisition of advanced knowledge in information management, especially in basic questions of the information economy, information systems and technology and innovation management. The learning outcome is the understanding of the processes and in the operational information management and the acquisition of competences in the use of information in an entrepreneurial context. This includes the ability to solve strategic, technical, organizational and legal problems in the use of information applications in companies and organizations.
The objective of the module is the acquisition of basic knowledge of models and methods of data visualization. Different methods of data visualization are treated, which should lead to an extended competence transfer for the development of simple visualizations.
The aim of the module is the acquisition of basic knowledge of processes, models and procedures of project management. Different perspectives of the project management are treated, which should lead to an extended competence mediation for the realization of complex projects.
The objective of the module is the acquisition of basic knowledge of models and methods of data visualization. Different methods of data visualization are discussed, which should lead to an extended competence transfer for the development of visualizations.
The aim of the module is the acquisition of advanced knowledge of the processes, models and methods of Human-Information Interaction.
|Department||FB20 / Computer Science|
Independent scientific development of a current topic in the field of visual analytics, trend analytics and visual trend analytics based on: Own literature research, guided by supervisors; Interpretation and classification of the results of the literature work, together with supervisor; Writing a written report on the selected topic (German or English), guided by the supervisor; Preparation of a lecture on the elaborated topic, guided by supervisors; Holding the lecture in front of a specialist audience; Feedback to the speakers on the lectures (including rhetoric, presentation techniques) and the technical discussion.