Machine Learning is a key area of applied mathematics that has significantly impacted the quality of human life in recent decades, ranging from internet search to clinical decisional support. Machine learning is founded on several mathematical principles including functional analysis, linear algebra, statistics, measure theory/algebra, graph theory, probability, numerical analysis and optimization. As machine learning is increasingly being used to make predictions in challenging real-world pattern recognition applications, reliable estimation of confidence of a system in its prediction remains a significant challenge. The Conformal Prediction framework is a recent development in machine learning to associate reliable measures of confidence with results in classification and regression. This framework is founded on the principles of algorithmic randomness, transductive inference and hypothesis testing, and has several desirable properties for potential use in various real-world applications, such as the calibration of the obtained confidence values in an online setting. Over the last few years, there has been a growing interest in applying this framework to real-world problems such as clinical decision support, medical diagnosis, sea surveillance, network traffic classification, and face recognition.
