MSc

Type of assessment: Group oral examination and individualized group report with a page limit. 60% oral exam, 40% report Exam duration: Oral exam: 30 minutes Aid: Presentation slides Evaluation: 7 step scale, external examiner General course objectives Experimentation in cognitive science is a young field, and many mysteries remain about human behaviour, perception, and performance. With the ever-growing amount of human data available, and development of data science and artificial intelligence, it is a crucial time for understanding how to collect, handle, and interpret human data. ...

Type of assessment: Oral examination and reports (hand in report first and the poster presentation to allow for questions based on reports) Aids: All - Evaluation: 7 step scale, internal examiner Learning objectives A student who has met the objectives of the course will be able to: Demonstrate knowledge of machine learning terminology such as likelihood function, maximum likelihood, Bayesian inference, feed-forward, convolutional and Transformer neural networks, and error back propagation. ...

Evaluation of exercises/reports. The grading is based on an overall evaluation of exercises (50%) and final project report (50%). Specifically, the grade is based on individualized group reports. Exam duration: Written exam: 4 hours Evaluation: 7 step scale, internal examiner General course objectives The course objective is to enable students to create visualizations of complex data sets and to apply common strategies for understanding the content of data sets (e.g. text, music, images, etc). ...

Background The UX Design Prototyping course enables students to develop functional User Experience design prototypes on a variety of mobile devices using lean prototyping methods. Course evaluation is based on reports, with an emphasis on documenting process steps that include qualitative and quantitative testing with users. Use of AI in the course Use of Generative AI will be allowed in the course and can be useful for e.g. idea generation, image generation, elements of prototyping, information search etc. ...

Course Objective To provide students with a comprehensive understanding of the principles, technologies, and strategies involved in designing and managing global communication networks. Target Audience Graduate students specializing in telecommunications, network engineering, or related fields. Use of generative AI in Assignments (which are part of the exam) Course content from course database The focus of the course will be on providing the students of a general understanding of the complexity of the core and transport infrastructure. It will contain topics such as network topologies, network layering and stratification, reference models, complex communication systems, transmission and multiplexing protocols and their internal relation, logical vs. physical networks, traffic characterization, network resilience, network control and management, node architecture, energy efficiency, quality of service (QoS) and Quality of Experience, and transition from legacy to next generation networks. Assignments, groupwork, peer-evaluation and presentations are an integral part of the course. ...