Stephen Downes

OK, it started as a joke, and it's a bit of a joke article: "Breakfast is a vector space. You can place pancakes, crepes, and scrambled eggs on a simplex where the variables are the ratios between milk, eggs, and flour. We have explored too little of this manifold. More breakfasts can exist than we have known." The concept here is that we have names for different vectors of the three basic ingredients. For example, 'Pancake' = {milk:0.5, flour:0.25, egg:0.25}. The vector space is the combination of all possible values of these three items. Why does this matter? A vector space allows us to make inferences. For example, if we're using three eggs, what are we probably making? If we're not using any eggs, what then? A vector space is also a probability space. This article goes in a different direction, searching for 'dark breakfast', where the probabilities of it being anything are low (think omelette with flour added). If you understand this, you're on the way to understanding machine learning. Via Data Science Weekly.