Slides

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Statistical Learning Theory, Generalization Errors, and Sampling Complexity Bounds: [PDF] [MicrosoftSlides]

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Complexity Measures, Radamacher, VC-Dimension: [PDF] [MicrosoftSlides]

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Support Vector Machines, Kernels, Optimization Theory Basics: [PDF] [MicrosoftSlides]

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Regression, Kernel Methods, Regularization, LASSO, Tomography: [PDF] [MicrosoftSlides]

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Unsupervised Learning, Dimension Reduction, Manifold Learning: [PDF] [MicrosoftSlides]

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Neural Networks and Deep Learning Basics: [PDF] [GoogleSlides]

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Convolutional Neural Networks (CNNs) Basics: [PDF] [GoogleSlides]

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Recurrent Neural Networks (RNNs) Basics: [PDF] [MicrosoftSlides]

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Generative Adversarial Networks (GANs): [PDF] [MicrosoftSlides]

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