Decoupled Classifiers for Group-Fair and Efficient Machine Learning

This course focuses on teaching decoupled classifiers for group-fair and efficient machine learning. The learning outcomes include understanding the concept of group fairness and efficiency in machine learning, as well as learning how to implement decoupled classifiers. The course teaches skills such as designing fair machine learning models and improving efficiency in classification tasks. The teaching method involves theoretical explanations and practical examples. This course is intended for individuals interested in machine learning, fairness, and efficiency in algorithmic decision-making.