Lecture videos (2017)
Earlier STATS 385 series — shows multi-year reuse of a course video archive.
Lecture 10 (2017): Papyan & Donoho — sparse coding view and ‘crimes of deep learning’.
Lecture 9 (2017): Bruno Olshausen — what’s still missing in deep learning.
Lecture 8 (2017): Joan Bruna — topology and geometry of ReLU network optimization.
Lecture 7 (2017): Jeffrey Pennington — random matrix theory for deep nets.
Lecture 5 (2017): Tomaso Poggio — when deep nets beat the curse of dimensionality.
Lecture 4 (2017): Ankit Patel — first-principles view of convnets.
Lecture 3 (2017): Helmut Bölcskei — harmonic analysis of deep convolutional nets.
Lecture 2 (2017): practical tour of deep learning before the theory deep-dives.
Lecture 1 (2017): Donoho/Monajemi/Papyan — framing the theory challenge.
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