Courses

Let me tell you something about the courses I take.

There are 3 required core courses and 2 electives, but one of the electives is required since I don’t have any design background.

The most important course of all is probably Human Computer Interaction Methods. This course, taught by Bonnie John and Chris Neuwirth, teaches us the main practices of a Human-Computer Interaction professional. It is an 18-credit course, which means 18 hours a week of work, but actually it takes more than that.

Because Methods is so much work, they invented the course HCI Pro Seminar, which is a weekly seminar session that gives an overview of the research and other accomplishments in the field of HCI. Everyone has to attend the seminar. Afterwards, half of the class has a discussion (and pizza) with the speaker, while the other half has to post two questions on the discussion board. The course is 6 credits, but takes far less than that. This way, it balances out with Methods.

Every MHCI student has to take either Software Architectures for User Interfaces (SAUI) or Programming User Interfaces (PUI). As a rule, students with an undergrad in Computer Science have to take SAUI. Now, I’m not a CS-major, but I decided that my programming is good enhough to be able to to SAUI. This means, however, that the course is quite hard for me!

Then there are two electives. But one is required, because I don’t have a design background. That course is called Communication Design Fundamentals. It teaches the very basics of how to do design, how to work with concepts like “balance”, “tension”, and “asymmetry” to convey a certain meaning. It is situated in the Apple Orchard, a room with over 50 iMacs! This the only course in which I can do absolutely nothing with my Innovation Science background, but that makes it even more exciting for me.

The other elective is truly my own choice. The course is called Applied Machine Learning. It teaches a technique called Data Mining, which is a way to organize, structure, predict and explain patterns in large datasets. You could say it’s like “really cool statistics by computers”. I really like the course, since it focusses not too much on the mathematical aspects of machine learning, but more on the practical aspects. Why do you use it? How do you use it? What can you do with it? Although the course is very “applied”, the teacher really encourages discussion.

I will put up more information about the specific courses as I go along. Time, however, is a very costly resource, and I don’t want to withhold you from details of the parties here, since I guess that’s far more interesting to most of you :D.

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