0This is the age of “big data.” Once upon a time, only companies had raw data. There used to be servers where that data was stored and processed. First with the arrival of personal computers followed by the
massive use of wireless communications, we all became generator of data. Each time we buy a product, each time we rent a movie, browse a web page, create a blog, or publish on the social media, even when we just walk or drive around, we are producing data. Each of us is not only a producer but also a consumer of data. We want specialized products and services for us. We want our requirements to
be analysed and modelled to be predicted. Think, for example, of a supermarket chain that is selling thousands of goods to millions of customers either at hundreds of brick-and-mortar stores all over a country or through a virtual store over the web. The details of each transaction are stored: date, customer id, goods bought and their amount, total money spent, and so forth. This typically amounts to a lot of data every day. What the supermarket chain wants is to be able to predict which customer is likely to buy which product, to maximize sales and profit. Similarly each customer wants to find the set of products best matching his/her needs. This task is not evident. We do not know exactly which people are likely to buy this ice cream flavor or the next book of this author, see this new movie, visit this city, or click this link. Customer behavior changes in time and by geographic location. But we know that it is not completely
random. People do not go to supermarkets and buy things at random. When they buy beer, they buy chips; they buy ice cream in summer and spices for Glühwein in winter. There are certain patterns in the data. 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. Predicting customer behavior is one; another is to tell spam emails from legitimate ones. 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. But we do not know how to transform the input to the output. What is considered spam changes in time and from individual to individual. What we lack in knowledge, we make up for in data. We can easily compile thousands of example messages, some of which we know to be spam and some of which are not, and what we want is to “learn” what constitutes spam from them. In other words, we would like the computer (machine) to extract automatically the algorithm for this task. There is no need to learn to sort numbers since we already have algorithms for that, but there are many applications for which we do not have an algorithm but have lots of data. We may not be able to identify the process completely, but we believe, we can construct a good and useful approximation. That approximation may not explain everything, but may still be able to account for some part of the data. We believe that though identifying the complete process may not be possible, we can still detect certain patterns or regularities. This is the niche of machine learning. Such patterns may help us understand the process, or we can use those patterns to make predictions: Assuming that the future, at least the near future, will not be much different from the past when the sample data was collected, the future predictions can also be expected to be right. Application of machine learning methods to large databases is called data mining. The analogy is that a large volume of earth and raw material is extracted from a mine, which when processed leads to a small amount of very precious material; similarly, in data mining, a large volume of data is processed to construct a simple model with valuable use, for example, having high predictive accuracy. Its application areas are abundant: In addition to retail, in finance banks analyze their past data to build models to use in credit applications, fraud detection, and the stock market. In manufacturing, learning models are used for optimization, control, and troubleshooting. In medicine, learning programs are used for medical diagnosis. In telecommunications, call patterns are analyzed for network optimization and maximizing the quality of service. In science, large amounts of data in physics, astronomy, and biology can only be analyzed fast enough by computers. The World Wide Web is huge; it is constantly growing, and searching for relevant information cannot be done manually. But machine learning is not just a database problem; it is also a part of artificial intelligence. To be intelligent, a system that is in a changing environment should have the ability to learn. If the system can learn and adapt to such changes, the system designer need not foresee and provide solutions for all possible situations.
Machine learning also helps us find solutions to many problems in vision, speech recognition, and robotics. Let us take the example of recognizing faces: This is a task we do effortlessly; every day we recognize
family members and friends by looking at their faces or from their photographs, despite differences in pose, lighting, hair style, and so forth. But we do it unconsciously and are unable to explain how we do it. Because we are not able to explain our expertise, we cannot write the computer program. At the same time, we know that a face image is not just a random collection of pixels; a face has structure. It is symmetric. There are the eyes, the nose, the mouth, located in certain places on the face. Each person’s face is a pattern composed of a particular combination of these. By analyzing sample face images of a person, a learning program captures the pattern specific to that person and then recognizes by checking for this pattern in a given image. This is one example of pattern recognition.
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. Machine learning 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 twofold: First, 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, 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.
