Will Ma defines “model-based” to construct independent distributions over time and solve dynamic programming, while “model-free” optimizes over historical trajectories in a black-box fashion.
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.
On Thursday, April 3, join AI Assistant Program Director Ayham Boucher for the debut of a bi-weekly series for Cornell students, faculty, and staff who want to know more about all things AI. Held over Zoom, the thirty minute workshop starts at 2pm ET.
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.
Held every other Friday from 4:00-4:30 PM via Zoom, the forum is for Cornell faculty and staff (e.g., research operational support staff) who are interested in the EMPIRE AI’s current status and future plans. Biweekly forums are scheduled throughout the Spring 2025 semester starting February 14.
Please register for the series, which will then provide the Zoom link info
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.
On Thursday, April 3, join AI Assistant Program Director Ayham Boucher for the debut of a bi-weekly series for Cornell students, faculty, and staff who want to know more about all things AI. Held over Zoom, the thirty minute workshop starts at 2pm ET.
Virtual lecture series on topics across machine learning in medicine, featuring extensive Q & A and panel discussions.
