## Robot Localization III: The Kalman Filter

This is part 3 in a series of articles explaining methods for robot localization, i.e. determining and tracking a robot's location via noisy sensor measurements. You should start with the first part: Robot Localization I: Recursive Bayesian Estimation This post [...]

## Dealing with Unbalanced Classes in Machine Learning

In many real-world classification problems, we stumble upon training data with unbalanced classes. This means that the individual classes do not contain the same number of elements. For example, if we want to build an image-based skin cancer detection system [...]

## Robot Localization II: The Histogram Filter

This is part 2 in a series of articles explaining methods for robot localization, i.e. determining and tracking a robot's location via noisy sensor measurements. You should start with the first part: Robot Localization I: Recursive Bayesian Estimation Idea The [...]

## Robot Localization I: Recursive Bayesian Estimation

This is part 1 in a series of tutorials in which we explore methods for robot localization: the problem of tracking the location of a robot over time with noisy sensors and noisy motors, which is an important task for every [...]

## “Can Computers Think?” -“No, but…”

This text deals with arguments against the possibility of so-called strong artificial intelligence, with a particular focus on the Chinese Room Argument devised by philosopher John Searle. We start with a description of the thesis that Searle wants to disprove. Then [...]

## Gödel’s Incompleteness Theorem And Its Implications For Artificial Intelligence

Introduction This text gives an overview of Gödel’s Incompleteness Theorem and its implications for artificial intelligence. Specifically, we deal with the question whether Gödel’s Incompleteness Theorem shows that human intelligence could not be recreated by a traditional computer. Sections 2 [...]