Networks are everywhere: traffic infrastructure and the internet come to mind, but networks are also in nature: food chains, protein-interaction networks, genetic interaction networks and of course neural networks which are being modelled by Artificial Neural Networks.
In this post, we will create a small network (also called graph mathematically) and ask some question about which is the “most important” node (also called vertex, pl. vertices). If you want to understand important concepts of network centrality and how to calculate those in R, read on!
Continue reading “Network Analysis: Who is the Most Important Influencer?”
Forecasting the future has always been one of man’s biggest desires and many approaches have been tried over the centuries. In this post we will look at a simple statistical method for time series analysis, called AR for Autoregressive Model. We will use this method to predict future sales data and will rebuild it to get a deeper understanding of how this method works, so read on!
Continue reading “Time Series Analysis: Forecasting Sales Data with Autoregressive (AR) Models”
Google does it! Facebook does it! Amazon does it for sure!
Especially in the areas of web design and online advertising, everybody is talking about A/B testing. If you quickly want to understand what it is and how you can do it with R, read on!
Continue reading “Learning Data Science: A/B Testing in Under One Minute”
It is not easy to create secure passwords. The best way is to let a computer do it by randomly combining lower- and upper-case letters, digits and other printable characters.
If you want to learn how to write a small function to achieve that read on!
Continue reading “Learning R: Build a Password Generator”
Our intuition concerning randomness is, strangely enough, quite limited. While we expect it to behave in certain ways (which it doesn’t) it shows some regularities that have unexpected consequences. In a series of seemingly random posts, I will highlight some of those regularities as well as consequences. If you want to learn something about randomness’ strange behaviour and gain some intuition read on!
Continue reading “Learning Statistics: Randomness is a Strange Beast”
Star Wars is somewhat nerdy, R definitely is… what could be more worthwhile than combining both 😉
This Sunday was Star Wars Day (May the 4th be with you!) and suitable for the occasion we will do a little fun project and implement the following xkcd flowchart, which can give us more than 2 million different Star Wars plots.
Even if you are new to R, the used code should be comprehensible, so read on!
Continue reading “Learning R: Build xkcd’s Star Wars Spoiler Generator”
In one of my most popular posts So, what is AI really? I showed that Artificial Intelligence (AI) basically boils down to autonomously learned rules, i.e. conditional statements or simply, conditionals.
In this post, I create the simplest possible classifier, called ZeroR, to show that even this classifier can achieve surprisingly high values for accuracy (i.e. the ratio of correctly predicted instances)… and why this is not necessarily a good thing, so read on!
Continue reading “ZeroR: The Simplest Possible Classifier… or: Why High Accuracy can be Misleading”
The global lockdown has slowed down mobility considerably. This can be seen in the data produced by our ubiquitous mobile phones.
Apple is kind enough to make those anonymized and aggregated data available to the public. If you want to learn how to get a handle on those data and analyze trends with R read on!
Continue reading “COVID-19: Analyze Mobility Trends with R”
One of the biggest problems of the COVID-19 pandemic is that there are no reliable numbers of infections. This fact renders many model projections next to useless.
If you want to get to know a simple method how to roughly estimate the real number of infections and expected deaths in the US, read on!
Continue reading “COVID-19 in the US: Back-of-the-Envelope Calculation of Actual Infections and Future Deaths”
Learning Machines proudly presents a guest post by Martijn Weterings from the Food and Natural Products research group of the Institute of Life Technologies at the University of Applied Sciences of Western Switzerland in Sion.
Continue reading “Contagiousness of COVID-19 Part I: Improvements of Mathematical Fitting (Guest Post)”