Robust Statistics Lecture
Many CVML scientists, engineers and enthusiasts do not have solid mathematical background, as it is so easy to jump into almost any CVML domain using available libraries and frameworks. This is very much true in Deep Learning and leads to a cacophony of inaccurate statements and a polyphony of ill-defined terms and concept. Therefore, a rigorous mathematical background is a must for anybody working in this area. Luckily, most ECE/CS curricula provide such foundations.
This lecture overviews Robust Statistics that has many applications in Data Analytics and Digital Signal Processing and Analysis. It covers the following topics in detail: Outliers. Measures of Robustness: Sensitivity Curve (SC), The Influence Function, Breaking Point. Robust Estimators: L – Estimators, M – Estimators, S – Estimators.