As machine learning is maturing, it has begun to make the successful transition from academic research to various practical applications. Generic techniques such as decision trees and artificial neural networks, for example, are now being used in various commercial and industrial applications. Learning to learn is an exciting new research direction within machine learning. However, the book investigates algorithms that can change the way they generalize, i.e., practice the last of learning itself, and improve on it. To illustrate the utility of learning to learn, it is worthwhile to compare machine learning to human learning.
Humans encounter a continual stream of learning tasks.
They do not just learn concepts of motor skills, they also learn bias, i.e., they learn how to generalize. A deeper understanding of computer programs that improve their ability to learn can have large practical impact on the field of machine learning and beyond. In recent years, the field has made significant progress towards a theory of learning to learn along with practical new algorithms, some of which led to impressive results in real-world applications. Its objective is to investigate the utility and feasibility of computer programs that can learn how to learn, both from a practical and a theoretical point of view. Creating Advice-Taking Reinforcement Learners , by Richard Maclin and Jude W. Shavlik Click here to order Back to my publications and my home page .