AI (I): Machine learning

Machine learning provides de foundation for artificial intelligence. So, what is it?

Machine learning is a technique in which we train a software model using data. The model learns from the training cases and then, we can use the trained model to make predictions for new data cases. To have a computer make intelligent predictions from the data, we just need a way to train it to perform the correct calculations.

We usually start with a data set that contains historical records, often called cases or observations. Each observation includes numeric features that quantify a characteristic of the item we are working with. We can call it ‘X’. In addition, we also have some value that we are trying to predict, we can call it ‘Y’. The purpose is to use our training cases to train a machine learning model so it can calculate a value for ‘Y’ from the features in ‘X’. As a simplification, we are creating a function that operates on a set of features ‘X’, to produce predictions ‘Y’.

Generally speaking, there are two broad kinds of machine learning, supervised and unsupervised.

In supervised learning scenarios, we start with observations called labels, that include known values for the variable we want to predict. The first thing we need to do, it is to split our data because we already know the label we are trying to predict. In this way, we can train the model using half of the data and keep the rest to test the performance of our model. When we obtain the desired results and we are confident our model works, we can use it with new observations for which the label is unknown, and generate new predicted values.

Unsupervised learning is different from supervised learning, in that this time we do not have known label values in the training data set. We train the model by finding similarities between the observations. After the model is trained, each new observation is assigned to the cluster of observations with the most similar characteristics.

AI (I): Machine learning

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