What is Machine Learning
The goal of building systems that can adapt to their environments and learn from their experience has attracted researchers from many fields, including computer science, engineering, mathematics, physics, neuro science, and cognitive science.
To solve a problem on a computer, we need an algorithm. An algorithm is a sequence of instructions that should be carried out to transform the input to output. For example, one can devise an algorithm for sorting. The input is a set of numbers and the output is their ordered list. For the same task, there may be various algorithms and we may be interested in finding the most efficient one, requiring the least number of instructions or memory or both. For some tasks, however, we do not have an algorithm for example, to tell spam emails from legitimate emails. We know what the input is: an email document that in the simplest case is a file of characters. We know what the output should be: a yes/no output indicating whether the message is spam or not. We don't know how to transform the input to the output. What can be considered spam changes in time and from individual to individual.
To solve a problem on a computer, we need an algorithm. An algorithm is a sequence of instructions that should be carried out to transform the input to output. For example, one can devise an algorithm for sorting. The input is a set of numbers and the output is their ordered list. For the same task, there may be various algorithms and we may be interested in finding the most efficient one, requiring the least number of instructions or memory or both. For some tasks, however, we do not have an algorithm for example, to tell spam emails from legitimate emails. We know what the input is: an email document that in the simplest case is a file of characters. We know what the output should be: a yes/no output indicating whether the message is spam or not. We don't know how to transform the input to the output. What can be considered spam changes in time and from individual to individual.
A subfield of computer science and artificial intelligence (AI) that
focuses on the design of systems that can learn from and make decisions
and predictions based on data. Machine learning enables computers to act
and make data-driven decisions rather than being explicitly programmed
to carry out a certain task. Machine Learning programs are also designed
to learn and improve over time when exposed to new data. Machine
learning has been at the center of many technological advancements in
recent years such as self-driving cars, computer vision and speech
recognition systems.
Machine learning is programming computers to optimize a performance criterion using example data or past experience. We have a model defined up to some parameters, and learning is the execution of a computer program to optimize the parameters of the model using the training data or past experience. The model may be predictive to make predictions in the future, or descriptive to gain knowledge from data, or both. It uses the theory of statistics in building mathematical models, because the core task is making inference from a sample. The role of computer science is in two ways: First one is in training, we need efficient algorithms to solve the optimization problem, as well as to store and process the massive amount of data we generally have. Second is, once a model is learned, its representation and algorithmic solution for inference needs to be efficient as well. In certain applications, the efficiency of the learning or inference algorithm, namely, its space and time complexity, may be as important as its predictive accuracy.
We need learning in cases where we cannot directly write a computer program to solve a given problem, but need example data or experience. for an example, where learning is necessary when human expertise does not exist, or when humans are unable to explain their expertise. Machine learning is programming computers to optimize a performance criterion using example data or past experience. We have a model defined up to some parameters, and learning is the execution of a computer program to optimize the parameters of the model using the training data or past experience. The model may be predictive to make predictions in the future, or descriptive to gain knowledge from data, or both. It uses the theory of statistics in building mathematical models, because the core task is making inference from a sample. The role of computer science is in two ways: First one is in training, we need efficient algorithms to solve the optimization problem, as well as to store and process the massive amount of data we generally have. Second is, once a model is learned, its representation and algorithmic solution for inference needs to be efficient as well. In certain applications, the efficiency of the learning or inference algorithm, namely, its space and time complexity, may be as important as its predictive accuracy.
Why we need Machine Learning
Consider the recognition of spoken speech that is, converting the acoustic speech signal to an ASCII text; we can do this task apparently without any difficulty, but we are unable to explain how we do it. Different people utter the same word differently due to differences in age, gender, or accent. In machine learning, the approach is to collect a large collection of sample utterances from different people and learn to map these to words.
Another example is an intelligent user interface that can adapt to the biometrics of its user namely, his or her pronunciation, handwriting, working habits, and so forth.The best example is Artificial Intelligence System.
There are number of cases where we cannot write program directly as result is based on previous experience or environment of the problems. Like, Weather Forecasting, Speech Recognition when human are enable to explain their expertise, Bioinformatics, Cheminformatics, Computer Networks, Computer vision including object recognition, Marketing, Natural Language Processing(NLP), Search Engines(Google, Bing), Language Translation and so on.
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