## Connectionist Models of Cognition

In this video, I give an introduction to the field of computational cognitive modelingĀ (i.e. modeling minds through algorithms) in general, and connectionist modeling (i.e. using artificial neural networks for the modeling) in particular. We deal with the following topics:The purpose [...]

## Robot Localization IV: The Particle Filter

This is part 4 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 The last [...]

## 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 forĀ 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 [...]