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Machine learning is a branch of artificial intelligence that includes methods, or algorithms, for automatically creating models from data. Unlike a system that performs a task by following explicit rules, a machine learning system learns from experience. Whereas a rule-based system will perform a task the same way every time (for better or worse), the performance of a machine learning system can be improved through training, by exposing the algorithm to more data.
Machine learning algorithms are often divided into supervised (the training data are tagged with the answers) and unsupervised (any labels that may exist are not shown to the training algorithm). Supervised machine learning problems are further divided into classification (predicting non-numeric answers, such as the probability of a missed mortgage payment) and regression (predicting numeric answers, such as the number of widgets that will sell next month in your Manhattan store).
Unsupervised learning is further divided into clustering (finding groups of similar objects, such as running shoes, walking shoes, and dress shoes), association (finding common sequences of objects, such as coffee and cream), and dimensionality reduction (projection, feature selection, and feature extraction)...
Neural networks and deep learning
Neural networks were inspired by the architecture of the biological visual cortex. Deep learning is a set of techniques for learning in neural networks that involves a large number of “hidden” layers to identify features. Hidden layers come between the input and output layers. Each layer is made up of artificial neurons, often with sigmoid or ReLU (Rectified Linear Unit) activation functions.
In a feed-forward network, the neurons are organized into distinct layers: one input layer, any number of hidden processing layers, and one output layer, and the outputs from each layer go only to the next layer.
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Source: InfoWorld
