Over the past 50 years, Machine Learning has grown from the efforts of a handful of computer engineers, exploring whether computers could learn to play games, to a broad discipline that has produced fundamental statistical-computational theories of learning processes.
A substantial amount of progress has been made in the development of learning algorithms, used in commercial systems for speech recognition, computer vision, and a variety of other tasks and it has spun off an industry in data mining to discover hidden regularities in the growing volumes of online data.
Many researches are being held in exploring the fundamental questions in Machine learning and in this article, we focus on some of those questions, which will change the face of machine learning significantly over the coming decade.
1. Can we build never-ending learners?
2. Can machine learning theories and algorithms help explain human learning?
3. Can we design programming languages containing machine learning primitives?
4. Will computer perception merge with machine learning?
To read more, download The Discipline of Machine Learning by Tom M. Mitchell
