RANSAC: Robust Outlier Detection and Applications

This lecture covers the Random Sample Consensus (RANSAC) algorithm, designed to handle outliers in data. The instructor explains how RANSAC works, its practical applications in Machine Learning, such as Structure from Motion and astrophotography, and its comparison with Bagging. Additionally, the lecture delves into using RANSAC for self-guiding vehicles and astrophotography, detailing the challenges faced in modeling sensor noise, light pollution, and lens defects. The presentation concludes with a summary of Bagging, Boosting, and Feature Selection techniques, highlighting their strengths and limitations.