Lecture 1: The Curse of Dimensionality

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Lecture 2: Geometric Stability in Euclidean Domains.

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Lecture 3: The Scattering Transform and Beyond

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Lecture 4: Non-Euclidean Geometric Stability and Graph Neural Networks

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Lecture 5: Graph Neural Network Applications

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Lecture 6: Unsupervised Learning under Geometric Priors

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Lecture 7: Discrete vs Continuous Time Optimization: The Convex Case

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Lecture 8: Discrete vs Continuous Time Optimization: Stochastic and Nonconvex case

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Lecture 9: Discrete vs Continuous Time Optimization: Stochastic and Nonconvex case

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Lecture 10: Nonconvex Optimization

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Lecture 11: Landscape of Optimization

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Lecture 12: Guest Lecture Behnam Neyshabur (IAS/NYU): Generalization in Deep Learning

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Lecture 13: Landscape of Optimization of Deep Neural Networks. Positive and Negative Results

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