Supervised vs. Unsupervised Learning: What they are and How they work

Unsupervised Learning is the class of algorithms that you can use that do not require labeled data to learn. This is what I've described above; there are mushrooms, and I can see the features of the mushroom (height, shape, color,) but I don't know if it's poisonous or not unless I eat it. What I can do, however, is start learning the different types of mushrooms, and combinations of features. For instance, maybe there is a common theme of red mushrooms that are taller than 3 inches, and another of green mushrooms with a very flat cap. Without knowing whether these mushrooms are poisonous or not, I'm able to make common groups of mushrooms or clusters of similar mushrooms (clustering is one of the more common types of unsupervised learning.)