Unlike data mining, ML has the ability to use computers to probe the data for structure, even if we do not have a theory of what the structure could look like. Whether these are simple algorithms using Bayesian analysis or complex neural networks or deep learning algorithms, ML applications have demonstrated success at recognizing patterns, recognizing anomalies, and in prediction. Available datasets can be used to seed ML models for categorization or clustering to identify different characteristics of infrastructure assets for analysis. Supervised learning can be used to supplement historical data with human intervention when the confidence in predictions is low. This can accelerate review processes and enable scarce expert resources to focus on outliers rather than entire datasets. Image recognition algorithms can be used to analyze images from aerial surveys for a large scale assessment of damage after a natural event and although this not provide information on the operability or performance for all forms of infrastructure, it can form a useful basis for on-the-ground mission planning activities.