This thesis provides a breadth-first tour of artificial intelligence techniques using ordinary data compression programs like zip. Using mathematical theory such as Kolmogorov Complexity and Shannon's Coding Theory, we arrive at a unique and generic perspective on universal learning with a plethora of real examples. Included are results from literature, astronomy, animal and virus evolution, linguistics, semantics, and music. An open source software package, CompLearn, is available for download so that interested readers may continue the research themselves in their own applications.