Machine learning |
Algorithms and statistical models that are programmed to learn from data, therefore recognizing and inferring patterns within them. This enables computers to perform specific tasks without explicit instructions from a human operator |
Supervised learning |
Refers to machine learning tasks whereby the goal is to identify a function that best maps a set of inputs (e.g. image) to their correct output (label). This is based learning or training on prematched pairs. This is in contrast to unsupervised learning, where novel patterns such as groups or ‘clusters’ are identified in data without influence from prior knowledge or labelling |
Overfitting |
A common problem in machine learning where the model has high accuracy when tested on data from the same source as its training data, but its performance does not generalize to novel sources of data |
Neural network |
A form of supervised machine learning inspired by biology whereby data pass through a series of interconnected neurons, which are individually weighted to make predictions. During training, the data pass through the network in an iterative manner and the weightings are continually adjusted to optimize its ability to match label to data |
Deep learning |
Refers to a neural network with multiple layers of ‘neurons’ that have adjustable weights (mathematical functions) |
Convolutional neural network |
Refers to a type of neural network whereby the layers apply filters for specific features to areas within an image |