In this course we study statistical pattern recognition and machine learning. After completing this course, the student: - knows important methods of statistical pattern recognition and their theoretical foundation; - knows general principles of statistical learning, such as over-fitting and the bias-variance decomposition; - knows how to apply methods of statistical pattern recognition to practical data analysis problems; - can perform an experimental evaluation of statistical learning methods in a sound manner; - knows the fundamental deep learning topics; - has practical skills for applying deep learning methods (e.g. feed forward neural networks, CNNs, RNNs); - and can model a research-oriented problem in the application domain of deep learning and suggest a solution using deep learning techniques.

This is a first year's course, where students learn about the tension Information Scientists find themselves in. They will learn to place themselves in the overlap between ICT, organisations, and society.

In this course, students have to work out an IT startup around the theme 'prevention'. Most notably this includes forming a business plan and delivering an MVP.

In this course, students learn about communication. This goes in three parts: human-human, human-computer, and computer-computer, laying a firm foundation for the Bachelor Information Sciences. As a TA, I was responsible for guiding students in both the building of a webshop and a research concerning e-commerce and trust. They had to use this as a basis for an advisory report delivered along side their custom built webshop demonstrating their recommendations.