Machine Learning – James Wilkins

Model Evaluation: Why Accuracy Isn’t Enough

Suppose we have a model that predicts the colour of a ball. We have 5 red balls and 5 blue balls and we ask our model to make a prediction of the colour of each of them. How can we evaluate our model’s success?
One approach would be to count the number of predictions that our model gets right. We count the number of red balls that the model predicts to be red (number of True Positives) and count the number of blue balls that our model predicts to be blue (the number of True Negatives).

Python Chess: Game Simulation and Illegal Moves

This is exciting! We’re nearly ready to start simulating a whole game of Chess. There’s just a few more components we need.
To begin, let’s first consider what information we need to encode to fully describe a move.

In standard chess move notation, we are given only the type of piece, and it’s new location. E.g. Ne2 which indicates that a knight should be moved to the square e2.

Image Segmentation With K-Means Clustering

Looking at the images above, we see an example of an image posterization filter that gives images a cartoon-like appearance, but behind the scenes, this filter is actually using a machine learning algorithm known as clustering.

Before exploring into how this process works and seeing how we can implement it in Python, let’s take a look at why we might want to do this in the first place.

Solving the Travelling Salesman Problem Using a Genetic Algorithm

The Travelling Salesman Problem, TSP, describes a scenario where a salesman wishes to visit a number of cities, while taking the shortest possible route, before returning home to the start point. While it may appear simple, this problem not only has no known polynomial time solution, but there is also no time-efficient way to prove that a given solution is in fact optimal.

The Machine Learning Algorithm Inspired by Darwin

In the ever-expanding field of artificial intelligence, some of the most fascinating advancements come from mimicking the natural world. One such breakthrough is the Genetic Algorithm, a powerful machine learning technique inspired by Charles Darwin’s theory of natural selection. Just as organisms evolve over time, honing their adaptations to thrive in their environments, Genetic Algorithms evolve solutions to complex problems, becoming increasingly effective with each iteration.