The talk gives a concise outline of some of the common mistakes that occur when using machine learning techniques, and what can be done to avoid them. It is intended primarily as a guide for research students and industry practitioners , and focuses on issues that are of particular concern to validate machine learning models, such as the need to do rigorous comparisons and reach valid conclusions. It covers five stages of the machine learning process: what to do before model building, how to reliably build models, how to robustly evaluate models, how to compare models fairly, and how to report results.
Tuesday, November 22, 2022 at 6:00 PM to
Tuesday, November 22, 2022 at 7:00 PM