Declarative Thinking and Programming

Before we actually dive into this topic, imagine the following: You just moved to a new place and the time is ripe for a little house-warming dinner with your best friends Alice and Bob. Since Alice is really tech-savvy you just send her a digital invitation with date, time and of course your new address that she can add to her calendar with a single click. With good old Bob is a bit more difficult, he is having a real struggle with modern IT. That’s why you decide to send him an e-mail not only including the time and location but also a suggestion which train to take to your city, details about the trams, stops, the right street and so. Weiterlesen

Anomaly Detection: (Dis-)advantages of k-means clustering

In the previous post we talked about network anomaly detection in general and introduced a clustering approach using the very popular k-means algorithm. In this blog post we will show you some of the advantages and disadvantages of using k-means. Furthermore we will give a general overview about techniques other than clustering which can be used for anomaly detection. Weiterlesen

Causal Inference and Propensity Score Methods

In the field of machine learning and particularly in supervised learning, correlation is crucial to predict the target variable with the help of the feature variables. Rarely do we think about causation and the actual effect of a single feature variable or covariate on the target or response. Some even go so far as to say that „correlation trumps causation“ like in the book „Big Data: A Revolution That Will Transform How We Live, Work, and Think“ by Viktor Mayer-Schönberger and Kenneth Cukier. Following their reasoning, with Big Data there is no need to think about causation anymore, since nonparametric models will do just fine using correlation alone. For many practical use cases, this point of view may seem acceptable — but surely not for all.