Lecture videos for STATS385, Fall 2017 Lecture01: Deep Learning Challenge. Is There Theory? (Donoho/Monajemi/Papyan) Lecture02: Overview of Deep Learning From a Practical Point of View (Donoho/Monajemi/Papyan) Lecture03: Harmonic Analysis of Deep Convolutional Neural Networks (Helmut Bolcskei) Lecture04: Convnets from First Principles (Ankit Patel) Lecture05: When Can Deep Networks Avoid the Curse of Dimensionality (Tomaso Poggio) Lecture06: Views of Deep Networks from Reproducing Kernel Hilbert Spaces (Zaid Harchaoui) Lecture07: Understanding and Improving Deep Learning With Random Matrix Theory (Jeffrey Pennington) Lecture08: Topology and Geometry of Half-rectified Network Optimization (Joan Bruna) Lecture09: What is missing in Deep Learning (Bruno Olshausen) Lecture10: Convolutional Neural Networks in View of Sparse Coding and Crimes of Deep Learning (Vardan Papyan and David Donoho) back