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.
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Virtual lecture series on topics across machine learning in medicine, featuring extensive Q & A and panel discussions. |
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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. |
1 event,
Virtual lecture series on topics across machine learning in medicine, featuring extensive Q & A and panel discussions. |
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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. |
2 events,
Virtual lecture series on topics across machine learning in medicine, featuring extensive Q & A and panel discussions.
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Join us for a roundtable discussion to learn about how people are already using AI in their humanities-related work. Featuring: Jan Burzlaff (Jewish Studies), Laurent Dubreuil (Comparative Literature), Sturt Manning (Classics), Jessica Ratcliff (Science & Technology Studies), & Adam T. Smith (Anthropology/CIAMS) |
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1 event,
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. |
1 event,
Virtual lecture series on topics across machine learning in medicine, featuring extensive Q & A and panel discussions. |
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1 event,
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. |
1 event,
Virtual lecture series on topics across machine learning in medicine, featuring extensive Q & A and panel discussions. |
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