Dimensionality reduction is a technique used in machine learning to reduce the number of features in a dataset. This can help to improve the performance of machine learning models by reducing the amount of noise and redundancy in the data, as well as making the data more computationally efficient to process.

Linear unmixing is a popular technique used for analyzing hyperspectral data. Hyperspectral imaging is a remote sensing technique that captures the electromagnetic radiation reflected from the Earth’s surface across hundreds of narrow spectral bands.

Financial index tracking is a popular investment strategy that involves investing in a portfolio of securities that mirrors the composition and performance of a particular financial index, such as the S&P 500 or the Dow Jones Industrial Average.

Semantic segmentation in remote sensing refers to the process of segmenting an image obtained from a remote sensing platform (such as a satellite or drone) into different regions or objects based on their semantic meaning.

Black-box machine learning models are now routinely used in high-risk settings, like medical diagnostics, which demand uncertainty quantification to avoid consequential model failures. Conformal prediction is a user-friendly paradigm for creating statistically rigorous uncertainty sets/intervals for the predictions of such models.

Data drift is a common problem in machine learning where the statistical properties of the data used for model training change over time. This can lead to a drop in model performance, as the model is no longer optimized for the new distribution of data.