Slides
Statistical Learning Theory, Generalization Errors, and Sampling Complexity Bounds: [PDF] [MicrosoftSlides]
Complexity Measures, Radamacher, VC-Dimension: [PDF] [MicrosoftSlides]
Support Vector Machines, Kernels, Optimization Theory Basics: [PDF] [MicrosoftSlides]
Regression, Kernel Methods, Regularization, LASSO, Tomography: [PDF] [MicrosoftSlides]
Unsupervised Learning, Dimension Reduction, Manifold Learning: [Large Slides] [PDF] [MicrosoftSlides]
Neural Networks and Deep Learning Basics:
[PDF] [GoogleSlides]
Convolutional Neural Networks (CNNs) Basics:
[PDF]
[GoogleSlides]
Recurrent Neural Networks (RNNs) Basics: [PDF]
[MicrosoftSlides]
Generative Adversarial Networks (GANs): [PDF]
[MicrosoftSlides]