Step behind the curtain and explore the engines of computation. This category breaks down the foundational algorithms and statistical models that allow machines to learn, predict, and sort the world. You’ll master core Machine Learning concepts, understand crucial evaluation metrics (because accuracy isn’t enough!), and discover how classic computational challenges are solved, from finding a stable match to detecting the edge of an image.
Ever noticed how your brain just knows where one object ends and another begins? That instinctive separation is a fundamental part of how we perceive the world. And computers? Well, they just need a bit more help.
In computer vision, edge detection is the process of teaching machines to recognise those boundaries, outlines, and transitions in intensity that define structure in an image.
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).
Explore the Gale-Shapley Algorithm and the Stable Matching Problem. Learn how algorithms create stable pairings, not just perfect matches.
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.
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.
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.
