It's nice MIT offers lectures like this and the gilbert strands lectures. Any other ML/Math related lectures recomended?
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One trend I'm seeing: I think the math that is becoming more useful, if huge foundation models are taking over, is around comparing and understanding their embedding/latent spaces. Geometry, topology, optimal transport, alignment -- in favor of statistics and probability theory
The classic 6.034 of [Classic] Artificial Intelligence from the late Prof. Patrick Winston is "mandatory".