Hi!

I am a researcher at the intersection of ML, Optimization and Manufacturing, currently employed as a PostDoc at the University of Augsburg. I will start as a professor for AI-based optimization in car manufacturing at the Technische Hochschule Ingolstadt in 2021.

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Coming from a software engineering background (I spent much time programming in Java and Python, drawing UML diagrams, devising user stories, etc.), I decided to share some examples/code etc. that I would have found helpful during my transition towards deep learning. That’s why I want to help connecting the dots. I have been teaching university classes on machine learning, combinatorial optimization, and self-organizing systems since 2013.

I am convinced that algorithms are taught most efficiently (time invested vs concepts understood) by example. Let’s work through what is actually computed. Then, dense mathematical notation becomes much easier to digest.

In the past, I also had fun writing models for combinatorial optimization problems (think timetabling or scheduling under hard and soft constraints) in MiniZinc and MiniBrass. Feel free to browse my dissertation.

Cheers, Alexander Schiendorfer