As a broad subfield of artificial intelligence, machine learning is 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 not only to data mining and statistics, but also theoretical computer science.

Applications

Machine learning has a wide spectrum of applications including natural language processing, syntactic pattern recognition, search engines, medical diagnosis, bioinformatics, brain-machine interfaces 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.

Human interaction

Some machine learning systems attempt to eliminate the need for human intuition in the analysis of the data, while others adopt a collaborative approach between human and machine. Human intuition cannot be entirely eliminated since the designer of the system must specify how the data is to be represented and what mechanisms will be used to search for a characterization of the data. Machine learning can be viewed as an attempt to automate parts of the scientific method.

Some statistical machine learning researchers create methods within the framework of Bayesian statistics.

We can find a lot of references about this topic. One of them is The Edimburgh Machine Learning Group.

The Edinburgh Machine Learning Group – part of the Institute for Adaptive and Neural Computation (ANC), School of Informatics. The group focuses on probabilistic and information theoretic approaches to machine learning problems. There is a significant emphasis on problems in image modelling/interpretation and stochastic processes, as well as an interest in manifold learning, clustering methods and signal analysis. Much of the group’s work is applied to problems of scientific inference, including research in areas of astronomy, remote sensing, meteorology, medical imaging, medical signal processing, neuro-informatics and bio-informatics.

This Machine Learning has strong associations with other groups within and out with the School of Informatics. It also has a paper discussion meeting approximately fortnightly. This is called the Probabilistic Inference Group (PIGS). As we can see, it is an interesting Machine Learning which has important and useful works.

Sources:

• Wikipedia, la enciclopedia libre. Última modificación: 28-04-08. Fecha de consulta: 05-05-08. http://en.wikipedia.org/wiki/Machine_learning
• The Edinburgh Machine Learning Group from http://www.anc.ed.ac.uk/machine-learning/