Intelligence and learning are intimately connected. They need each other to be at their peak. This is why, since its inception in the fifties, Artificial Intelligence has been preoccupied with the study of learning, as testified by the pioneering works devoted to the first cybernetic" turtles" or" mouses", or the CHECKERS program [1]. Aside from this fundamental interest in learning that, in a way, dates back to the Greek philosophers, there are more practical reasons why" intelligent" systems should be endowed with learning capacities. Numerous tasks are indeed intrinsically quite difficult to program, if only because they imply seemingly infinite numbers of unpredictable situations. For example, in the domain of pattern recognition or obstacle avoidance, it is impossible to enumerate all possible cases. Likewise, the knowledge acquisition phase has long been identified as a major problem in the development of knowledge-based systems. The prospect of a machine capable of automatically acquiring the competencies needed to face up new and unknown situations is therefore quite attractive. Furthermore, there is a need for systems that can adapt in face of changing environments, improve their performance and update their knowledge while carrying on their duties. Here too, learning is required, possibly involving longer timescale. Finally, there are activities like scientific discovery that are learning problems in themselves. For all these reasons, machine learning is an active field of research experiencing a vigorous development.This chapter is intended to provide an introductory survey of some achievements in machine learning and of the main issues that are currently of central concern.