Software Engineer,AI Student,Web Developer,Aspiring ML Engineer
Things I'm (becoming) an expert at
During my previous study programs, I received a training in advanced statistics. Turns out Machine Learning is basically applied statistics! No wonder I kept specializing in this fascinating field.
I appreciate good theory, but getting my hands dirty and building web applications is one of the most enjoyable things. I especially love Django and Flask, and I'm also learning React.
Python is awesome. It allows you to do almost anything, and it's a great tool for both of my passions: Machine Learning and Web Development. I've already tutored a university level Python course for beginners.
I love dealing with data, both in a theoretical/analytical way (Data Science) and in a more practical way (Data Engineering). My favorite database management system is PostgreSQL.
Although I dismissed a research career, I still find my background in Psychology and Neuroscience very helpful when dealing with AI topics. Those fields have great potential of informing and inspiring each other.
I love sharing my knowledge with others, and I have taught various university-level classes and seminars (Statistics, Research Methods, Python). It's true what they say: If you can't explain it simply, you don't understand it well enough.
Some of my personal projects - with more to follow soon!
I created an AI agent who learns to play the game Nim. The user can decide how many rounds a new agent is allowed to play against itself in order to learn. Try it out and read my medium article for more details.
To try out Django and PostgreSQL, I used data from the subreddit "r/AmItheAsshole", in which Reddit users describe a situation and ask the community whether they believe the original author had behaved like an asshole. On my web app, users are asked to guess the community's verdict - asshole or not an asshole?
I used the famous Titanic data set from Kaggle.com and analyzed which features made it more likely for a passenger to survive. Specifically, I applied an XGBoost algorithm and explained the resulting model with the new SHAP value method. Read my data science report here.
For five years, I studied human intelligence and decision making. After completing a B.Sc. in Psychology and a research M.Sc. in Neuroeconomics, the logical next step would have been a PhD.
However, I didn't want to end up working as a researcher. Why? Because I love building stuff!
That's why I'm currently doing another B.Sc. in Software Engineering at CODE University Berlin. My new passion is Machine Learning and its practical applications. My career goal is becoming a Machine Learning Engineer.
When I'm not coding, I love writing and playing music, playing tennis, and learning chess.
Find out more on LinkedIn!