SOW-MKI66
Advanced Academic & Professional Skills
Course infoSchedule
Course moduleSOW-MKI66
Credits (ECTS)6
CategoryMA (Master)
Language of instructionEnglish
Offered byRadboud University; Faculty of Social Sciences; Artificial Intelligence;
Lecturer(s)
Coordinator
dr. I.L. Camerino
Other course modules lecturer
Lecturer
dr. I.L. Camerino
Other course modules lecturer
Contactperson for the course
dr. I.L. Camerino
Other course modules lecturer
Examiner
prof. dr. R.G.J. Meulenbroek
Other course modules lecturer
Academic year2023
Period
SEM1  (04/09/2023 to 26/01/2024)
Starting block
SEM1
Course mode
full-time
Remark
Please note: if you do not yet have a master's registration, you are not yet registered for the tests for this course.
Remarks-
Registration using OSIRISYes
Course open to students from other facultiesNo
Pre-registrationNo
Waiting listNo
Placement procedure-
Aims

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 gained a better overview of the professional work field for AI
  • 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 better communication and interaction skills and
  • have learned how to overcome conflicts at the workplace.
  • have better insight into team dynamics.
Content

Module 1
Lectures and workgroups in the 1st module (mostly during the first quarter) 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 module, each group of 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.
Module 2
The core content in the 2nd module (mostly during the second quarter) 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, and in various fields of AI applications. They will also learn about team dynamics, project management, and time management. The course will also contain an introduction and training in 21st-century skills. The students have the chance of practicing these methods and skills in their course assignments and also the projects and assignments of the parallel courses. At the end of the 2nd period, each individual student will write a reflection report on their vision of a future career, the skills they have learned, and how these skills contributed to their activities including the assignments of this course and other courses in the period.  

Level
AI-MA
Presumed foreknowledge

Test information
  • Active participation 10%, including participating in lectures and workgroups/workshops and handing in all the formative assignments.
  • Group research proposals 40%, no minimum grade (no retake, the proposal will be graded after revision)
  • Presentation of research proposal 10%, no minimum grade (no retake)
  • Review report: fail/pass (communicated through Brightspace, retake possible)
  • Letter to reviewers: fail/pass (communicated through Brightspace, retake possible)
  • Personal reflection report 40%, no minimum grade (retake possible)
In order to pass the course, students must obtain at least 60% of the scores and have passed all the fail/pass assignments. Moreover, the students cannot miss more than 50% of lectures and 50% of the workgroup sessions.

Grading for each part is registered in Brightspace, only the final grade is published in Osiris.
 
Specifics
Please sign up for any course at (https://portal.ru.nl/home), it is obligatory.

Students who are enrolled for a course are also provisionally registered for the exam. 

Resit: Manual register at (https://portal.ru.nl/home) until five working days prior to the date of the exam. No delayed registration is possible. 

We urge you to always read the course information on Brightspace. 
 
Instructional modes
Lecture

General
Lectures

Working group
Attendance MandatoryYes

Tests
Final grade
Test weight100
OpportunitiesBlock SEM1, Block SEM1