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In high school I liked Math because it showed me pure logic. I liked how all Math principles could follow up on each other, calculations were like puzzles to solve. When I first had to code at uni I was quite overwhelmed by all the new unknown aspects of it. When I got the hang of it I really could be working on it for hours until I had what I wanted. It was not necessarily easy for me but I had to make it work like a puzzle that needs to be solved.


By now I have worked with/in Processing, Arduino, Python, NetLogo and CIF. Coding showed me how you can translate aspects of the world into a system and gather new insights from that.

Today I am still following one course, Intelligent interactive systems. This is bringing me a one step further than making models and analyze them. This course is teaching me how to use learning algorithms to make those models smart.


I am interested in working with manipulating material structures like in metamaterials. Therefor I want to gain knowledge in being able to create models of structures that are adaptable and if necessary based on relevant calculations. 

I would also be interested in implementing my new found knowledge in machine learning in projects to give them more character by being adaptable to their environment. For example making Bloomn learn from the user and therefore being able to cooperate better.

For the course Rational Agents, the assignment was to make the robot in NetLogo rational. The robot had to be programmed to get to the white box in the least amount of steps within its energy. With every step, the robot lost energy, also by encountering the randomly moving red smileys, the robot should be programmed to lose energy. The happy smileys will attack the enemies and the green blocks give energy, the red boxes are obstacles. 

I was able to make the system fully working. To me this really felt like a puzzle. You had to think ahead and think out every possibility. I had to understand how the robot responds to a changing environment and to calculate if it has enough energy to proceed or if its better to go to an energy station. 

This taught me to apply logical thinking and to understand the basics of rational robots.

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To use a model to predict how a system will work

For the course Model based system engineering, I was assigned to simulate a realtime workstation. I learned to make a visualization of the system behave so that the packages with the right colors got from the pusher to the conveyor to the next workstation. By making the visualization work I could predict that the realtime system would be rightfully controlled by my code.

With some trial and error I have tested a code that controlled the system correctly.

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To obtain data
and get useful data out of it

For the course Making sense of sensors the assignment was to answer a health related question by gathering data with a sensor and make sense of that.

Our question was: Is there a relation between emotion and the amount of steps taken.

We gathered data by letting users where a MiBand for 10 working days and  them noting their emotions 2 times a day (using the SAM scale). We then used python to clean and order the data to plot it in different ways.

the result was no clear correspondence between the emotion and the amount of steps during a day period of an office worker. 

It taught us that a negative result is a result as well and that i is important to be aware that real world testing does not always result in reliable data. For example, one of our users got an ankle injury for a view days and the MiBand did not always measure correctly. You have to keep these things into account If you want to trust your findings.

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