Home 2017-09-13T02:40:09+00:00
2410, 2017

Connectionist Models of Cognition

October 24th, 2017|Categories: Artificial Intelligence, Cognitive Science, Computational Cognitive Modeling, Deep Learning, Machine Learning|

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 [...]

410, 2017

Robot Localization IV: The Particle Filter

October 4th, 2017|Categories: Artificial Intelligence, Machine Learning, Robotics, Self-Driving Cars|

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 [...]

2209, 2017

Robot Localization III: The Kalman Filter

September 22nd, 2017|Categories: Artificial Intelligence, Robotics, Self-Driving Cars|

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 [...]

1609, 2017

Dealing with Unbalanced Classes in Machine Learning

September 16th, 2017|Categories: Deep Learning, Keras, Machine Learning, Python|

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 [...]

1509, 2017

Robot Localization II: The Histogram Filter

September 15th, 2017|Categories: Artificial Intelligence, Machine Learning, Robotics, Self-Driving Cars|

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 [...]

909, 2017

Robot Localization I: Recursive Bayesian Estimation

September 9th, 2017|Categories: Artificial Intelligence, Machine Learning, Robotics, Self-Driving Cars|

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 [...]

Stay updated

Subscribe to the mailing list and get updated about new blog posts by email.

Thank you for subscribing.

Something went wrong.

Follow me on Twitter