Patna Women's College
What is Machine Learning?
The machine learning being done today is generally based on some sort of observations or data, such as examples (the most common case), direct experience, or instruction. In general, machine learning is about learning to do better in the future, based on what was experienced in the past.
The emphasis of machine learning is on automatic methods. In other words, the goal is to devise learning algorithms that do the learning automatically without human intervention or assistance. The machine learning paradigm can be viewed as ‘programming by example’. We may have a specific task in mind, such as spam filtering. But rather than program the computer to solve the task directly, in machine learning, we seek methods by which the computer will come up with its own program based on examples that we provide.
Machine learning is a core subarea of artificial intelligence. It is very unlikely that we will be able to build any kind of intelligent system capable of any of the facilities that we associate with intelligence, such as language or vision, without using learning to get there. These tasks are otherwise simply too difficult to solve. Further, we would not consider a system to be truly intelligent if it were incapable of learning since learning is at the core of intelligence.
Although a subarea of AI, machine learning also intersects broadly with other fields, especially statistics, but also mathematics, physics, theoretical computer science and more.
Examples of Machine Learning Problems
There are many examples of machine learning problems. Much of this course will focus on classic cation problems in which the goal is to categorize objects into a fixed set of categories. Here are several examples:
- Optical Character Recognition: categorize images of handwritten characters by the letters represented
- Face Detection: Find faces in images (or indicate if a face is present) spam filtering: identify email messages as spam or non-spam
- Topic Spotting: categorize news articles (say) as to whether they are about politics, sports, entertainment, etc.
- Spoken Language Understanding: within the context of a limited domain, determine the meaning of something uttered by a speaker to the extent that it can be classified into one of a fixed set of categories
- Medical Diagnosis: diagnose a patient as a sufferer or non-sufferer of some disease
- Customer Segmentation: predict, for instance, which customers will respond to a particular promotion
- Fraud Detection: identify credit card transactions (for instance) which may be fraudulent in nature
- Weather Prediction: predict, for instance, whether or not it will rain tomorrow
Although much of what we will talk about is classic citation problems, there are other important learning problems. In classification, we want to categorize objects into fixed categories. In regression, on the other hand, we are trying to predict a real value. For instance, we may wish to predict how much it will rain tomorrow. Or, we might want to predict how much a house will sell for.
A richer learning scenario is one in which the goal is actually to behave intelligently, or to make intelligent decisions. For instance, a robot needs to learn to navigate through its environment without colliding with anything. To use machine learning to make money on the stock market, we might treat investment as a classic citation problem (will the stock go up or down) or a regression problem (how much will the stock go up), or, dispensing with these intermediate goals, we might want the computer to learn directly how to decide to make investments so as to maximize wealth.
Goals of Machine Learning Research
The primary goal of machine learning research is to develop general purpose algorithms of practical value. Such algorithms should be efficient. As computer scientists, we care about time and space efficiency. But in the context of learning, we also care a great deal about another precious resource, namely, the amount of data that is required by the learning algorithm.
Learning algorithms should also be as general purpose as possible. We are looking for algorithms that can be easily applied to a broad class of learning problems, such as those listed above.
Of course, we want the result of learning to be a prediction rule that is as accurate as possible in the predictions that it makes.
Occasionally, we may also be interested in the interpretability of the prediction rules produced by learning. In other words, in some contexts (such as medical diagnosis), we want the computer to do prediction rules that are easily understandable by human experts.
As mentioned above, machine learning can be thought of as ‘programming by example’. What is the advantage of machine learning over direct programming? First, the results of using machine learning are often more accurate than what can be created through direct programming. The reason is that machine learning algorithms are data driven and are able to examine large amounts of data. On the other hand, a human expert is likely to be guided by imprecise impressions or perhaps an examination of only a relatively small number of examples.
Also, humans often have trouble expressing what they know, but have no difficulty labeling items. For instance, it is easy for all of us to label images of letters by the character represented, but we would have a great deal of trouble explaining how we do it in precise terms.
Another reason to study machine learning is the hope that it will provide insights into the general phenomenon of learning. Some of the questions that might be answered include:
- What are the intrinsic properties of a given learning problem that make it hard or easy to solve?
- How much do you need to know ahead of time about what is being learned in order to be able to learn it effectively?
- Why are ‘simpler’ hypotheses better?