Wikipedia defines Parrondo’s paradox in game theory as
A combination of losing strategies becomes a winning strategy.
If you want to learn more about this fascinating topic and see an application in finance, read on!
Continue reading “Parrondo’s Paradox in Finance: Combine two Losing Investments into a Winner”
In data science, we try to find, sometimes well-hidden, patterns (= signal) in often seemingly random data (= noise). Pseudo-Random Number Generators (PRNG) try to do the opposite: hiding a deterministic data generating process (= signal) by making it look like randomness (= noise). If you want to understand some basics behind the scenes of this fascinating topic, read on!
Continue reading “Pseudo-Randomness: Creating Fake Noise”
When you ask successful people for their advice on how to become successful you will often hear that you have to take risks, often huge risks.
In this post we will examine whether this is good advice with a simple multi-agent simulation, so read on!
Continue reading “How to be Successful! The Role of Risk-taking: A Simulation Study”
The German news magazine DER SPIEGEL has a regular puzzle section in its online version, called “Rätsel der Woche” (“Riddle of the Week”). Some of those puzzles are quite interesting but I am often too lazy to solve them analytically.
So I often kill two birds with one stone: having fun solving the puzzle with R and creating some new teaching material for my R classes! This is what we will do with one of those more interesting riddles, which is quite hard to solve analytically but relatively easy to solve with R, so read on!
Continue reading “R Coding Challenge: How many Lockers are Open?”
More and more companies use chatbots for engaging with their customers. Often the underlying technology is not too sophisticated, yet many people are stunned at how human-like those bots can appear. The earliest example of this was an early natural language processing (NLP) computer program called Eliza created 1966 at the MIT Artificial Intelligence Laboratory by Professor Joseph Weizenbaum.
Eliza was supposed to simulate a psychotherapist and was mainly created as a method to show the superficiality of communication between man and machine. Weizenbaum was surprised by the number of individuals who attributed human-like feelings to the computer program, including his own secretary!
If you want to build a simple Eliza-like chatbot yourself with R read on!
Continue reading “ELIZA Chatbot in R: Build Yourself a Shrink”
The workhorse of Machine Learning is Gradient Descent. If you want to understand how and why it works and, along the way, want to learn how to plot and animate 3D-functions in R read on!
Continue reading “Why Gradient Descent Works (and How To Animate 3D-Functions in R)”
In this year’s end post I will give you a little programming challenge!
Everybody knows the Christmas song “The Twelve Days of Christmas”! Your task is to write an R script that creates the lyrics!
Continue reading “Learning R: Christmas Coding Challenge”
We already covered Neural Networks and Logistic Regression in this blog.
If you want to gain an even deeper understanding of the fascinating connection between those two popular machine learning techniques read on!
Continue reading “Logistic Regression as the Smallest Possible Neural Network”
xkcd webcomics is one of the institutions of the internet, especially for the nerd community. If you want to learn how to fetch JSON data from a REST API, download a file from the internet and display a PNG file in a ultra-simple example, read on!
Continue reading “xkcd Comics as a Minimal Example for Calling APIs, Downloading Files and Displaying PNG Images with R”
Everybody knows the Simpsons, everybody loves the Simpsons and everybody can laugh about Bart Simpson writing funny lines on the blackboard! If you want to create your own Bart Simpson Blackboard Meme Generator with R read on!
Continue reading “Create Bart Simpson Blackboard Memes with R”