Study programme

The course work contains 4 compulsory courses:
 

• Data Mining 

Data Mining is the process of looking for valuable pieces of information in large datasets. It helps identifying trends and patterns, establishing relationships, and creating actionable information. This is done by exploiting the most powerful hardware, the most powerful programming systems, and the most efficient algorithms to solve tasks from science, healthcare, government, education, commerce, energy, humanities, and many other fields. After this course you know how several well-known data mining algorithms work, how and when they can be applied, and how the resulting models and patterns should be interpreted. Furthermore, you understand general problems of data-analysis and gain practical experience with programming and application of data mining algorithms.

• Knowledge and Data Engineering

• Scientific Methods for Computing Science 

• Data Intensive Systems

Data has been coined “The oil of the 21st century”. Businesses and organizations have realized that in order to thrive in the data driven economy, they have to adopt modern data management solutions that  will allow them to innovate and generate high-quality added value services. Unfortunately the amount of data we collect nowadays has increased exponentially not only in size but also in complexity, semantics and speed. There is a need for advanced tools, techniques and methodologies in order to turn them into valuable insights. The goal of the course is to introduce you into the principles upon which the systems that manage and analyze massive volume datasets are built, make them familiar with their use, identify their capabilities and limitations, and enable them to propose and often implement improvements.

The elective training distincts primary and secondary electives.

Primary electives are programme related electives. You must select 5 courses out of a pre-defined list:

• Multimedia Retrieval
• Probabilistic reasoning
• Pattern recognition & Deep Learning
• Data Visualization
• Machine Learning in Dynamic Data Environments

Conventional supervised machine learning and data mining approaches often implicitly assume that the past is a good predictor of the future. This course challenges this view, as machine learning systems often have to operate in a dynamically changing and non-stationary world. The course aims to add another dimension to students' knowledge in machine learning: Time.

• Cloud and Edge Computing

Cloud computing is often depicted as an accelerator in realizing computing as the fifth utility. Such applications encompass complex workflows consisting of thousands to millions of devices involving complex big data generation, gathering, storage, and analysis in real-time. The growing complexity of cloud usage and management presents challenges related to sustainability, scalability, resource elasticity, and management across various levels of cloud services. These challenges led to the emergence of Edge computing. There is a pressing need to comprehensively understand and leverage both cloud and edge paradigms. This course aims to familiarize yourself with the fundamental concepts and principles underlying integrated cloud and edge computing architectures across different abstraction levels, including programming, runtime and performance models. You will gain the skill to develop and analyze  real-world cloud and edge native applications, while learning to apply the best practices and common pitfalls associated with designing, developing and deploying these systems using cutting-edge tools like Dockers, Kubernetes, Kafka, and Prometheus.

• Advanced machine learning
• Network science
• Process Mining
• Algorithms for decision support
• Algorithmic Data Analysis (Algorithmic Data Analysis (INFOMADA) will NOT be taught in the academic year 2023/2024)

Secondary electives can be considered any master course offered by any department of the university with at least 7.5 ECTS, that has not been used as mandatory or primary elective. You have room for 1 course.

In order to expand the thematic range of your Master's programme, you can also choose to do a profile in your (elective) course part. A profile is a coherent set of courses of 30 EC on a single theme that is usually not a standard part of the programme. Within this Master's you can choose from the following profiles:

• Complex systems profile