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.
Formulate relevant professional problems and hypotheses, which can limit and guide the project.
Plan and conduct a realistic project, make work plans and time schedules, and adjust the plan according to new conditions and acquired knowledge.
Choose and apply reasonable methods for modelling and design of intelligent systems, including ethical dimensions and societal impact.
Design and carry out experiments to evaluate and improve intelligent systems.
Evaluate and summarize results.
Write a structured technical report, including references and citations.
Provide constructive criticism of own and other’s work.
Present methods and results concisely both orally and in written form.
Use AI tools to optimally enhance all parts of the project work from searching literature, to brainstorming project scope and design and assisting in writing processes including report and code.
Document the use of the AI tools and reflect upon how you have critically evaluated the tools when assisted in the project work.
Content
The project content is chosen by the students and the teacher in common. Project subjects are published in previous semesters. The students may suggest projects themselves and find supervisors at the DTU departments for Applied Mathematics and Computer Science. The projects will typically include one or more of the disciplines machine learning, statistics, cognitive systems, signal processing, image analysis, computational social science, logic and algorithms, with applications in a wide range of societal domains. Besides the actual project work, there will be given instructions in report writing, group work, short intensive teaching in specific topics including the use of AI tools in research and project work and more. The course is finished by oral presentations for all participants and an oral examination. The presentations will take place at the end of the 13-week period and at the end of the 3-week period.
[1] https://www.linkedin.com/pulse/why-generative-ai-considered-disruptive-technology-education
[2] See e.g. https://teaching.cornell.edu/generative-artificial-intelligence
[3] https://uwaterloo.ca/centre-for-teaching-excellence/catalogs/tip-sheets/preparing-tests-and-exams
[4] See also Elsevier’s policy https://www.elsevier.com/about/policies-and-standards/the-use-of-generative-ai-and-ai-assisted-technologies-in-writing-for-elsevier