DTU Compute

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....

Exam: Written examination and reports Written exam (weight 40%) and individualized group report (weight 60%) Participation in project work activities are mandatory. Exam duration: Written exam: 2 hours Aid: One handwritten page (provided or own notes?) Evaluation: 7 step scale , external examiner General course objectives To give the participants a basic knowledge of · defining aspects of intelligent systems, · applications of intelligent systems in image, audio, text and game data,...

Type of assessment: Oral examination and reports Evaluation: 7 step scale General course objectives To qualify and train students in project work and provide a deeper understanding of one or more subjects within modelling, design and evaluation of intelligent systems. Learning objectives A student who has met the objectives of the course will be able to: Set up own learning objectives for the project work. Limit a subject area, suitable for a project of 10 ECTS, within modelling, design and evaluation of intelligent systems....

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....

Participants: Jes Frellsen, Chaudhary Ilyas, Rasmus Ørtoft Aagaard, A. Emilie Wedenborg, Tommy Alstrøm, Qianliang Li, Søren Føns, Lina Skerath, Finn Årup Nielsen, Jakob Eg Larsen, Susanne Winter, Ivana Konvalinka, Tobias Andersen, Søren Hauberg, Teresa Scheidt, Kristoffer Stensbo-Smidt, Lenka Tetkova, Camilla Narine, Mikkel N Schmidt, Georgios Avanitidis, Vagn L Hansen, Kyveli Kompatsiari, Fabian Mager, Vassilis Lyberatos, Morten Mørup, Nicki Skafte, Federico Delusso, Hiba Nassar, Hanlu He, Beatrix Miranda Nielsen, Lasse Skytte Hansen, Sune Lehmann, Laura Alessandretti, Jonas Vestergaard, Tiberiu-Ioan Szatmari, Antonio Desiderio, Laurits Fredsgaard, Per Bækgaard, Lars Kai Hansen...