Computer Science

Agile software development methodologies and frameworks have changed how software is created, and are widely used and supported. This is not surprising, given that agile approaches stand, among other aspects, for continuous change and collaboration between stakeholders. These characteristics are aligned with the dynamic needs of business models pursuing innovation, which is why companies consider agile software development a key element for the future. In this seminar we will explore the rise and evolution of agile software development. Among other aspects, we will look at the principles and values behind it, what differentiates it from traditional software development approaches, its main frameworks and methodologies, the challenges jeopardizing its values, and what we can expect from it for the years to come.

Participants learn basic concepts, their theoretical foundation, and the most common algorithms used in machine learning and artificial intelligence. After completing the module, participants understand strengths and limitations of the different paradigms, are able to correctly and successfully apply methods and algorithms to real world problems, are aware of performance criteria, and are able to critically evaluate results obtained with those methods. More specifically, participants are able to demonstrate: 1) Understanding regarding basic concepts of neural information processing 2) Knowledge of unsupervised machine learning methods 3) Application to problems of statistical modeling, explorative data analysis, and visualization. Topics include 

1) Principal Component Analysis, Kernel-PCA 

2) Independent Component Analysis (Infomax, FastICA, Second Order Blind Source Separation) 

3) Stochastic Optimization 

4) Clustering, Embedding, and Visualisation (Central and Pairwise Clustering, Self-Organizing Maps, Locally Linear Embedding) 

5) Density Estimation, Mixture Models, Expectation-Maximization Algorithm, Hidden Markov Model 

6) Estimation Theory, Maximum Likelihood Estimation, Bayesian Model Comparison

This course provides an overview of the history and technological evolution of computer games, experience related technologies and project planning. Furthermore, it studies VR (Virtual Reality) and AR (Augmented Reality) technologies and addresses the future of computer games. 

The course covers the following topics: 

・History of computer games 
・Technologies of computer games 
・Academic research of computer games 
・Hardware of entertainment system 
・Computer graphics 
・Motion capture system 
・Virtual reality 
・Augmented Reality, etc. 

This course includes topics such as user-centric design, user study design, data analysis, and verbal and non-verbal robot behavior. Additionally, the course explores several human-robot interaction applications such as healthcare, education, and in-home robots.

In recent years Machine Learning has started to influence all aspects of human life, and education is no exception. In this seminar course, we will introduce basic concepts of machine learning and education and learn how Machine Learning is employed nowadays to solve day-to-day problems, which are the most common in higher education. The problems include data manipulation, feature engineering, drop-out prediction and visualisation of student characteristics. Students will learn basics of Machine Learning using one of the most prominent Data Science languages R in the context of higher education data.

Participants explore software security hands-on with the goal to develop and host an international information security contest (¨Attack/Defense CTF”): contesting teams from all over the world receive virtual machines built during the project. The machines run participants’ services, containing secret tokens ("flags") that other teams have to collect over the wire using exploits as part of the game. To build the contest, participants will dive deep into the security of a platform and language of their choice and create a software project with well-hidden software vulnerabilities in this language. Furthermore, a game server will be developed as a team, including scripts to check the health of services for each contestant. As part of the development and hosting, participants will develop and extend the infrastructure required to host the competition, strengthen their skills in penetration testing and exploitation, and build upon other technical and non-technical abilities, depending on their role in the project. Such skills may include networking, continuous integration, agile development, project management and public relations. Furthermore, students develop and extend the infrastructure, required for the competition. The course gives participants the freedom to explore tools of their choice, build software and find creative ways to corrupt it, with the work done both independently and in small teams. Insecure software is a potential threat to both the industry and the democratic society. The course supports goals on sustainability by raising awareness on IT security, and teaching the ability to detect, fix and avoid security issues in software, not only for the students, but also for the international participants of the competition. Furthermore, we support open-source, by making all material publicly available in the end.

This course will introduce fundamental concepts and techniques in the content of remote sensing and image processing for Earth observation from space. The course starts by introducing core concepts in remote sensing (describing the processes by which images are captured by sensors mounted on satellite and airborne platforms and key characteristics of the acquired images). Then, fundamental methodologies for processing, analyzing, and visualizing remotely sensed imagery are introduced. Topics include representation of high-dimensional remote sensing images, time domain representations, filtering and enhancement. Practical applications will be provided throughout the course. Participants of this course will gain theoretical and practical knowledge on fundamental concepts and techniques for processing and analysis of remote sensing images acquired by Earth observation satellite and airborne systems.

This is a project-based course where students work in a team to carry out the development and management of a relatively large scale software project, building a piece of software to fulfil the needs of a particular customer. Students put into practice state-of-the-art techniques used in industrial software development to ensure that their team produces software cooperatively, reliably, and on schedule. Each team works on a different project, and receives individual coaching to provide support and advice relevant to their particular project.

In this course, students study the principles of computer networking, analyze and discuss the OSI & TCP/IP models, demonstrate how a network is designed based on specific requirements, and learn basic principles of computer security.