Machine learning is focused on writing software that can learn from past experience. One of the standard definitions of machine learning, as given by Tom Mitchell, a professor at the Carnegie Mellon University (CMU), is the following:
A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E.
For example, a computer program that learns to play chess might improve its performance as measured by its ability to win at the class of tasks involving playing chess, through experience obtained by playing chess against itself. In general, to have a well-defined learning problem, we must identify the class of tasks, the measure of performance to be improved, and the source of experience. Consider that a chess-learning problem consists of the following: task, performance measure, and training experience, where:
- Task T is playing chess
- Performance measure P is the percentage of games won against opponents
- Training experience E is the program playing practice chess games against itself
To put it in simple terms, if a computer program is able to improve the way it performs a task with the help of previous experience, this way you will know the computer has learned. This scenario is very different from one where a program can perform a particular task because its programmers have already defined all the parameters and have provided the data required to do so. A normal program can perform the task of playing chess because the programmers have written the code to play chess with a built-in winning strategy. However, a machine learning program does not possess a built-in strategy; in fact, it only has a set of rules of the legal moves in the game, and what a winning scenario is. In such a case, the program needs to learn by repeatedly playing the game until it can win.
Is machine learning applicable to all scenarios? When exactly should wehave the machine learn rather than directly programming the machine with instructions to carry out the task?
Machine learning systems are not knowledge-based systems. In knowledge-based systems, we can directly use the knowledge to codify all possible rules to infer a solution. We go for machine learning when such codification of instructions is not straightforward. Machine learning programs will be more applicable in the following scenarios:
- Very complex tasks that are difficult to program: There are regular tasks humans perform, such as speaking, driving, seeing and recognizing things, tasting, and classifying things by looking at them, which seem so simple to us. But, we do not know how our brains are wired or programmed or what rules need to be defined to perform all this seamlessly, for which we could create a program to replicate these actions. It is possible through machine learning to perform some of them, not to the extent that humans do, but machine learning has great potential here.
- Very complex tasks that deal with a huge volume of data: There are tasks that include analyzing huge volumes of data and finding hidden patterns, or coming up with new correlations in the data, that are not humanly possible. Machine learning is helpful for tasks for which we do not humanly know the steps to arrive at a solution and which are so complex in nature due to the various solution possibilities that it is not humanly possible to determine solutions.
- Adapting to changes in environment and data: A program hardcoded with a set of instructions cannot adapt itself to the changing environment and is not capable of scaling up to new environments. Both of these can be achieved using machine learning programs.
Note
Machine learning is an art, and a data scientist who specializes in machine learning needs to have a mixture of skills—mathematics, statistics, data analysis, engineering, creative arts, bookkeeping, neuroscience, cognitive science, economics, and so on. He needs to be a jack of all trades and a master of machine learning.