This video by Udacity summarizes Supervised vs Unsupervised Learning very nicely.

In a nutshell (using clustering as an example for instance):

• Supervised Learning has label. e.g. Spam email filtering. We have 100 emails filled with words. We label each email SPAM (Junk email) or HAM (email that worth reading) up-front. We apply algorithms (such as Naive Bayes) to build a model that will tell us, given a new email, how probable that the email is a SPAM.
• Unsupervised Learning has NO label. The algorithm will try and label it for you. (e.g. divid the 100 emials into two cluster. The SPAM cluster may share some common characteristics. The HAM cluster may have some other characteristics. (e.g. K-means Clustering, Expectation Maximization Clustering, Spectral Clustering).