What do you like about the specialisation and why?
Coming from a Bachelor's in Artificial Intelligence, this Master's programme is very applied, which is what I had hoped for. The programme has a lot of projects, which often times gives you some freedom to choose a direction and a challenge level that suits you (assuming your team agrees). This same freedom comes with challenges. You receive much less guidance than in the bachelor and often times there's no 'right answer' to be found in the slides, which means you need to get used to taking the initiative yourself for many courses.
What do you think about the atmosphere in class?
In general, I think many teachers are very open and easy to approach. They feel more like equals with more experience than superiors, which is wonderful and one of my favourite things about Radboud University. I feel like the students themselves are on average also much more motivated than in the Bachelor's, which is an absolute blessing considering the amount of group work.
What do you find most challenging in your Master’s specialisation?
Many courses require you to 'teach yourself' to some degree, which is something I wasn't used to. The workload for some courses is also very high, and the prerequisite knowledge listed for several courses is sometimes outdated. This led me to take some courses that I would otherwise have taken later in the curriculum, after other courses, or even not at all.
Are you currently doing an internship? Or what is your thesis about?
Neither. I am in my first year, and I'm still not sure what direction I want to go in. Could be language-related, could be nature-related, could be ethics-related, could be something else. I have diverse interests.
What are your plans once have received your Master's degree?
I honestly have no idea what exactly I want to do with it. I want to solve real-world problems, but I'm not sure which. That's actually one of the reasons I chose this Master's: it's very widely applicable in many domains. Gaining a deeper understanding of, or solving a problem with data is something highly valuable everywhere where they have data. Which, again, is everywhere.