by Van E. Neie (1985)
To the student: Physics is problem solving. Unfortunately, this fact often is taken to mean that one must perform numerical calculations, a process known more colloquially as "plug and chug." In the real sense, however, a problem solving means grappling with a new idea or a posed question or problem in reaching a satisfactory solution, be it a numerical value or a verbal statement. The purpose of this guide is to help you improve your problem-solving skills through a variety of tasks designed to foster successful techniques and strategies. To be successful, you must become actively engaged in the process. Problem solving cannot be learned by watching, any more than one can learn to play the violin by watching a violinist. There is simply no shortcut to proficiency in this skill. Without practice, progress will be minimal.
Patricia Murphy and Kathy Hall
This is one of the course readers for the Open University course 'Curriculum Learning and Society' (E846). The book emphasizes the socially negotiated and embedded nature of meaning-making and how learners learn to use the cognitive tools of their cultural community through participation in social activity.
Ming Li, Akira Maruoka (eds
This book constitutes the refereed proceedings of the 8th International Workshop on Algorithmic Learning Theory, ALT'97, held in Sendai, Japan, in October 1997. The volume presents 26 revised full papers selected from 42 submissions. Also included are three invited papers by leading researchers. Among the topics addressed are PAC learning, learning algorithms, inductive learning, inductive inference, learning from examples, game-theoretical aspects, decision procedures, language learning, neural algorithms, and various other aspects of computational learning theory.
Sebastian Thrun, Lorien Pratt
Over the past three decades or so, research on machine learning and data mining has led to a wide variety of algorithms that learn general functions from experience. 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. Similar to traditional machine-learning algorithms, the methods described in Learning to Learn induce general functions from experience. However, the book investigates algorithms that can change the way they generalize, i.e., practice the task of learning itself, and improve on it. To illustrate the utility of learning to learn, it is worthwhile comparing machine learning with human learning. Humans encounter a continual stream of learning tasks. They do not just learn concepts or motor skills, they also learn bias, i.e., they learn how to generalize. As a result, humans are often able to generalize correctly from extremely few examples - often just a single example suffices to teach us a new thing. A deeper understanding of computer programs that improve their ability to learn can have a 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. Learning to Learn provides a survey of some of the most exciting new research approaches, written by leading researchers in the field. 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.
Louise and Richard Spilsbury
Introduces some of the animals that call the ocean home, discussing how they find food, what kind of structures they build to live in, and the threats to their habitat.