Education Material

Our lecture topics and modules aim to cover topics such as known biases in AI and strategies to mitigate them, methods to evaluate data on ETAI, and fundamental concepts in AI/ML tailored to different audiences. The lectures will also cover statistical design, data wrangling, feature extraction and selection, modeling techniques, and model evaluation. There will be 1-2 lectures per domain-specific topic, such as clinical background or established standards, and case studies will be used to illustrate ideal use cases and potential applications. We plan to also include tutorials on AI tools and project management, as well as discussions of practical considerations, such as alignment with government policies and common misunderstandings and pitfalls. Finally, we aim to foster transdisciplinary collaborations and ensure operationalization and real-world usage of ETAI. To begin browsing our educational content, please visit our Video Library