Tools like the Wolfram Language have made machine learning accessible to anyone with basic computing skills. As the importance of knowing the internal details of machine learning algorithms diminishes, the importance of understanding how to apply machine learning appropriately rises. This talk explores the issues you need to consider in making data-driven decisions. It discusses topics such as when machine learning is appropriate, sources of bias, validation and explainability of models and decision-making criteria.
Director of Technical Services, Communication and Strategy