This course consists of two modules. The first module is "Research Topics and Academic Writing" and the second module is "Professional skills". Each module offers distinct learning goals:
Research Topics and Academic writing module: At the end of this module, the student will
- have learned different research methodologies in AI
- have learned how to formulate research questions in AI
- be able to critically read and evaluate academic articles
- be able to translate theory into a practical framework, algorithm, or computational model.
- have learned how to write better academic English and
- have learned how to evaluate their own writing and that of others.
Professional skills module: At the end of this module the students
- have learned about the non-technical skills needed for an AI and ML engineer in the professional work field.
- know about teamwork and communication techniques in the workplace.
- have learned about project management and time management.
- have gained more insight into their communication style.
- have gained better communication and interaction skills
- have learned how to overcome conflicts at the workplace
- have better insight into team dynamics
Lectures and workgroups in the 1st block will prepare the students for academic writing and research. in the lectures, the students will be introduced to the art of academic writing. They will be introduced to the differences between and the challenges of writing different kinds of documents, including: shorter and longer research articles, grant proposals, CVs, reviews, and letters to the editor. In the meantime, during the workgroup activities, the students explore a variety of topics and methodologies in AI. They will be introduced to five different research themes and have the possibility of discussing the topic and methodologies with the guest lecturers. Students then will subscribe to one of the AI research themes and will have an active discussion on new research ideas. At the end of the 1st block, each group of 5 students will write a research proposal in the field of AI. Close attention should be paid to the embedding of the research into existing literature, the rationale of the research and consistency of argumentation, the formulation of research questions and hypotheses, and the design of a proper methodology. The proposals will then be reviewed by the other students following the course and the teachers. Students will revise their sample on the basis of these reviews. They will then send in the rewritten sample with a “letter to the editor” explaining how they have dealt with the issues raised by their reviewers.
The core content in the 2nd block is professional skills and is mainly focused on outside academia. During the lectures, the students will be introduced to the recruitment procedures for jobs in the industry and learn about working in various non-academic jobs such as working in big companies, start-ups, non-profit and government organizations. They will also learn about project management and time management. The course will also contain an introduction and training of 21st-century skills. In the second half of the course, each group of students will pick a small size project and practice the methods and skills while conducting the project. At the end of the 2nd block, each group of students delivers one project report and each individual student will write a reflection report on the skills they have learned.
Adapted test information following the Corona situation will be communicated in Brightspace.|
In order to pass the course, students must obtain at least 60% of the scores and have passed all the fail/pass assignments.
- Group research proposals 40%, no minimum grade (no retake)
- Presentation of research proposal 10%, no minimum grade (no retake)
- Review report: fail/pass (communicated though brightspace, retake possible)
- Revision: fail/pass (communicated though brightspace, retake possible)
- Group project report 10%, no minimum grade (no retake)
- Personal reflection report on professional skills 40%, no minimum grade (retake possible)
Grading for the different parts will be made public in Brightspace. Only the final grade will be published in Osiris at the end of the course.