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)