Machine Learning in Medicine
VirtualVirtual lecture series on topics across machine learning in medicine, featuring extensive Q & A and panel discussions.
Virtual lecture series on topics across machine learning in medicine, featuring extensive Q & A and panel discussions.
The Cornell Learning Machines Seminar is a semi-monthly seminar held at the Cornell Tech campus in New York City. The seminar focuses on machine learning and related areas, including Natural Language Processing, Vision, and Robotics.
Virtual lecture series on topics across machine learning in medicine, featuring extensive Q & A and panel discussions.
The Cornell Learning Machines Seminar is a semi-monthly seminar held at the Cornell Tech campus in New York City. The seminar focuses on machine learning and related areas, including Natural Language Processing, Vision, and Robotics.
Virtual lecture series on topics across machine learning in medicine, featuring extensive Q & A and panel discussions.
Virtual lecture series on topics across machine learning in medicine, featuring extensive Q & A and panel discussions.
Join us in learning how a self-driving biomaterials lab powered by AI and automation is transforming material design to better integrate with biological systems, presented by Adam Gormley, Associate Professor of Biomedical Engineering at Rutgers University.
Over two days of programming with some of the biggest names in health, AI, and technology, attendees will emerge with an actionable roadmap for successful innovations in healthcare.
One promising application of AI in educational settings is the generation of individualized feedback that can support learners in their learning process, even for complex tasks such as writing essays. In this talk, Jennifer Meyer, Assistant Professor for Educational Diagnostics and Counselling in Schools at the Centre for Teacher Education at the University of Vienna, will present empirical results on the effectiveness of AI-generated feedback, the role of learners’ individual differences for feedback engagement, learner perceptions of AI-generated feedback compared to human feedback, as well as the effects of hallucinated feedback on student learning outcomes.