Learning
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In the fields of neuropsychology, personal development and education, Learning is one of the most important mental function of humans, animals and artificial cognitive systems. It relies on the acquisition of different types of knowledge supported activities such as play, seen only in relatively intelligent animals[1][2] and humans. Therefore, in general, a learning can be conscious and not conscious.
For example, for small children, non-conscious learning processes are as natural as breathing. In fact, there is evidence for behavioral learning prenatally, in which habituation has been observed as early as 32 weeks into gestation, indicating that the central nervous system is sufficiently developed and primed for learning and memory to occur very early on in Developmental Psychologydevelopment.[3]
From the social perspective, learning is the goal of teaching and education.
Conscious learning is a capacity requested by students, therefore is usually goal-oriented and requires a motivation.
Learning has also been mathematically modeled using a differential equation related to an arbitrarily defined knowledge indicator with respect to time, and dependent on a number of interacting factors (constants and variables) such as initial knowledge, motivation, intelligence, knowledge anchorage or resistance, etc.[4][5]
Machine learning
Although learning is often thought of as a property associated with living things, computers are also able to modify their own behaviors as a result of experiences. Known as machine learning, this is a broad subfield of artificial intelligence concerned with the design and development of algorithms and techniques that allow computers to "learn". At a general level, there are two types of learning: inductive, and deductive. Inductive machine learning methods extract rules and patterns out of massive data sets.
The major focus of machine learning research is to extract information from data automatically, by computational and statistical methods. Hence, machine learning is closely related to data mining and statistics but also theoretical computer science.
Machine learning has a wide spectrum of applications including natural language processing, syntactic pattern recognition, search engines, medical diagnosis, bioinformatics and cheminformatics, detecting credit card fraud, stock market analysis, classifying DNA sequences, speech and handwriting recognition, object recognition in computer vision, game playing and robot locomotion.
See also
- Animal cognition
- Developmental Psychology
- History of education
- Intelligence
- Machine learning
- Pedagogy
- Reasoning
- Sequence learning
- Sleep and learning
- Study skills
References
- ^ Jungle Gyms: The Evolution of Animal Play
- ^ What behavior can we expect of octopuses?
- ^ Sandman, Wadhwa, Hetrick, Porto & Peeke. (1997). Human fetal heart rate dishabituation between thirty and thirty-two weeks gestation. Child Development, 68, 1031-1040.
- ^ Fadul, J. "Mathematical Formulations of Learning: Based on Ten Learning Principles" International Journal of Learning. Volume 13 (2006) Issue 6. pp. 139-152.
- ^ deFigueiredo, R.J.P. Mathematical formulation of cognitive and learning processes in neural networks, 1990
- Mayer, R. E. (2001). Multimedia learning. New York: Cambridge University Press. ISBN 0-52178-749-1.
- Paivio, A (1971). Imagery and verbal processes. New York: Holt, Rinehart, and Winston.
- Holt, John (1983). How Children Learn. UK: Penguin Books. ISBN 0140225706
External links
- Education & Learning List of selected links
- How to be a Better Learner
- Pedagogy Iran