T = the task of playing chess .
P = the probability that the program will win the next game .
Broadly , machine learning problems can be classified as : Supervised learning and Unsupervised learning .
Supervised Learning
In supervised learning , we know both the input variable say ‘ x ’ and the correct output say ‘ y ’ and we also have the knowledge that there is a relationship between the input and the output . These problems can be of two types : regression and classification problems . In a regression problem , we try to predict results within a continuous output . In other words , we try to map input variables to some continuous function . Instead , in a classification problem , we try to predict results in a discrete output . In other words , we try to map input variables into discrete categories .
Unsupervised Learning
In unsupervised learning , we have the input variable say ‘ x ’ but have little or no idea about correct output . All that we do is derive a structure from that data where we don ’ t necessarily know the effect of the variables . This is done by clustering the data based on relationships among the variables in the data .
Further explanation or discussion would require mathematics and so we dwell no further in this direction . Readers interested in the topic can choose a simple course from MOOC for further reading . However , before moving , let us understand the reasons for machine learning being so prevalent today . In fact , machine learning is a field that has grown out of the field of artificial intelligence , commonly known as AI .
In the field of AI , the scientist while attempting to build intelligent machines found that they can programme a machine to do some basic things such as how to find the shortest path from A to B but did not know how to write AI programmes to do more interesting and complex things . Scientists realized that the only way to do these things was to have the machine learn to do it by itself . So , machine learning was developed as a new capability for computers and today it touches many segments of industry and basic science . Range of problems that machine learning touches is far and wide such as robotics , computational biology and tons of things in Silicon Valley . For example , when every time one goes to Amazon or Netflix or iTunes Genius , it recommends the movies or products and music to buy and that ’ s machine learning algorithm at work . However , there are about million users ; one cannot write million different programmes for million users . The only solution that one can think of is that the software has to learn by itself to customize the preferences .
In a slightly dated article accessed over internet that listed top IT skills , at the top of this list of the twelve most desirable IT skills was machine learning . At least in US , a number of recruiters contact universities and enquire about graduating machine learning students . Apparently , there seems to exist a vast , unfulfilled demand for this skill set , and this is a great time to learn about machine learning .
Neural Networks
Neural Network is also a machine learning algorithm . Neutral network was actually an old idea , but had fallen out of favor for a while . But today , it is the state of the art technique for many different machine learning problems . But one might wonder as to the
14 DYNAMISM ( E )