What is Machine learning.
Machine learning is the Science of Getting computers to act without directive programming them. Learning in this context is not learning actually but recogniziong Complex patterns and make intelligent decisions based on data. Machine learning develops algorithm that Discover knowledge from specific data and experience. Based on sound statical and computation principles. It evolved from the study of pattern recoginition and computational learning theory in artificial intelligence, machine learning exploring the study and construction of algorithm that can learn from and make prediction on data , such a logarithm overcome following is strictly statics program instruction by making data driven prediction for decisions, through building a model from sample inputs. It is considered to be the driving force for speech recognition Technologies , self- driving Cars ,effective of focused web search, and a vastly improved understanding of the human genome.
History of machine learning.
The history of the field of machine learning is a fascinating story. In 1946 the first computer system ENIAC was developed. At that time the world word ‘computer’ meant are you human being that perform numerical computation on paper and ENIAC was called our numerical computing machine.
This machine was manually operated that is human would make connections between parts of the machine to perform computation. The idea at that time was that human thinking and learning could be rendered logically in such a machine. In 1950 Allen Turing proposed a test to measure it performance. The Turing test it is based on that Idea that we can only determine if a machine can actually learn if we communicate with it and cannot distinguish it from another human. Althrough, there have not been any system that passed the Turing test.
Around 1952 Arthur Samuel (IBM) wrote the first game-playing program, for checkers, to achieve sufficient kill to challenge a world champion. Samuel’s machines learning programs worked remarkably well and were helpful in improving the performance of checker players. Another milestone was the ELIZA system developed in the early 60’s by joseph Weizenbaum. ELIZA simulated a psychotherapist by using tricks like string substitution and cannot response based on keywords.
Although the overall performance ELIZA was disappointing it was nice proof of concept. Later on many other systems have been developed. Important was the work of the group of Ted short life on (Stanford). They demonstrated the power of rule-based system for knowledge representation and interference and domain medical diagnosis and therapy. This system is often called the first expert system.
At the same time when the expert system was developed, other approaches to machine learning emerged. In 1957 Frank Rosenblatt invented the perceptron at the Cornell Aeronautical laboratory. The perception is very simple linear classifier but it was shown that by combining a large number of them in a network a model could be created.
Network research band through many years of stagnation Marvin Minsky and his colleagues showed that neutral address code not solve problem such as the XOR problem. However, several modifications has been produced later on that solve XOR and many more difficult problems.
In the early 90’s Machine learning become very popular again due to the section of computer science and statistics. The synergy resulted in a new way of thinking in AI: the probabilities approach. In this approach uncertainty in the parameter is operated in the models. The field shifted to a more data-driven approach as compared to the more knowledge-driven experts system developed earlier. Many of the current success stories of Machine Learning are the result of the ideas developed at that time.
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