KNOWING A MACHINE'S LANGUAGE

Machine learning is the science of getting computers to act without being explicitly programmed. Machine learning is applied in various fields such as computer vision, speech recognition, NLP, web search, biotech, risk management, cyber security, and many others.

The machine learning paradigm can be viewed as “programming by example”. Two types of learning are commonly used: supervised and unsupervised. In supervised learning, a collection of labeled patterns is provided, and the learning process is measured by the quality of labeling a newly encountered pattern. The labeled patterns are used to learn the descriptions of classes which in turn are used to label a new pattern. In the case of unsupervised learning, the problem is to group a given collection of unlabeled patterns into meaningful categories.

Within supervised learning, there are two different types of labels: classification and regression. In classification learning, the goal is to categorize objects into fixed specific categories. Regression learning, on the other hand, tries to predict a real value. For instance, we may wish to predict changes in the price of a stock and both methods can be applied to derive insights. The classification method is used to determine if the stock price will rise or fall, and the regression method is used to predict how much the stock will increase or decrease.

Let's say you want to predict housing prices. A while back a student collected data sets. and let's say you plot the data set and it looks like this.

Here on the horizontal axis, the size of different houses in square feet, and on the vertical axis, the price of different houses in thousands of dollars. So, given this data, let's say you have a friend who owns a house that is say 750 square feet, and they are hoping to sell the house, and they want to know how much they can get for the house. So, how can the learning algorithm help you? One thing a learning algorithm might be want to do is put a straight line through the data, also fit a straight line to the data. Based on that, it looks like maybe their house can be sold for maybe about $150,000. But maybe this isn't the only learning algorithm you can use, and there might be a better one. For example, instead of fitting a straight line to the data, we might decide that it's better to fit a quadratic function. If you do that and make a prediction here, then it looks like, well, maybe they can sell the house for closer to $200,000. There's no fair picking. But each of these would be a fine example of a learning algorithm. So, this is an example of a Supervised Learning algorithm. The term Supervised Learning refers to the fact that we gave the algorithm a data set in which the, called, "right answers" were given. That is we gave it a data set of houses in which for every example in this data set, we told it what is the right price.

So hopefully, you got that right. This is the answer to resource allocation.