A spectrum of machine learning tasks
	• Low-dimensional data (e.g. less than 100 dimensions) • Lots of noise in the data • There is not much structure in the data, and what structure there is, can be represented by a fairly simple model. • The main problem is distinguishing true structure from noise.				• High-dimensional data (e.g. more than 100 dimensions) • The noise is not sufficient to obscure the structure in the data if we process it right. • There is a huge amount of structure in the data, but the structure is too complicated to be represented by a simple model. • The main problem is figuring out a way to represent the complicated structure so that it can be learned.