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from IPython.display import Image
Image(filename='images/phd053104s.png')
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What is machine learning ?
A learning problem considers a set of n samples of data and then tries to predict properties of unknown data. If each sample is more than a single number and, for instance, a multi-dimensional entry (aka multivariate data), is it said to have several attributes or features.
We can separate learning problems in a few large categories:
- Supervised learning, in which the data comes with additional attributes that we want to predict.
- classification: samples belong to two or more classes and we want to learn from already labeled data how to predict the class of unlabeled data.
- regression: if the desired output consists of one or more continuous variables, then the task is called regression. An example of a regression problem would be the prediction of the length of a salmon as a function of its age and weight.
- Unsupervised learning, in which the training data consists of a set of input vectors x without any corresponding target values. The goal in such problems may be to discover groups of similar examples within the data, where it is called clustering, or to determine the distribution of data within the input space, known as density estimation, or to project the data from a high-dimensional space down to two or three dimensions for the purpose of visualization.
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from IPython.display import Image
Image(filename='images/instalacion.png')
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