Slides
Statistical Learning Theory, Generalization Errors, and Sampling Complexity Bounds: [PDF] [MicrosoftSlides]
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Complexity Measures, Radamacher, VC-Dimension: [PDF] [MicrosoftSlides]
⚠ <iframe src="https://onedrive.live.com/embed?cid=85808E8A085197E0&resid=85808E8A085197E0%215707&authkey=AKd1Oh949encoKY&em=2&wdAr=1.7777777777777777" width="1186px" height="691px" frameborder="0">This is an embedded ⚠ <a target="_blank" href="https://office.com">Microsoft Office⚠ </a> presentation, powered by ⚠ <a target="_blank" href="https://office.com/webapps">Office⚠ </a>.⚠ </iframe>
Support Vector Machines, Kernels, Optimization Theory Basics: [PDF] [MicrosoftSlides]
⚠ <iframe src="https://onedrive.live.com/embed?cid=85808E8A085197E0&resid=85808E8A085197E0%215710&authkey=ANymYB4ubCzxuV8&em=2&wdAr=1.7777777777777777" width="1186px" height="691px" frameborder="0">This is an embedded ⚠ <a target="_blank" href="https://office.com">Microsoft Office⚠ </a> presentation, powered by ⚠ <a target="_blank" href="https://office.com/webapps">Office⚠ </a>.⚠ </iframe>
Regression, Kernel Methods, Regularization, LASSO, Tomography: [PDF] [MicrosoftSlides]
⚠ <iframe src="https://onedrive.live.com/embed?cid=85808E8A085197E0&resid=85808E8A085197E0%215712&authkey=ACOk5uGNKWdw7xk&em=2&wdAr=1.7777777777777777" width="1186px" height="691px" frameborder="0">This is an embedded ⚠ <a target="_blank" href="https://office.com">Microsoft Office⚠ </a> presentation, powered by ⚠ <a target="_blank" href="https://office.com/webapps">Office⚠ </a>.⚠ </iframe>
Unsupervised Learning, Dimension Reduction, Manifold Learning: [Large Slides] [PDF] [MicrosoftSlides]
⚠ <iframe src="https://onedrive.live.com/embed?cid=85808E8A085197E0&resid=85808E8A085197E0%215718&authkey=AJw9agZaP0r4iU0&em=2&wdAr=1.7777777777777777" width="1186px" height="691px" frameborder="0">This is an embedded ⚠ <a target="_blank" href="https://office.com">Microsoft Office⚠ </a> presentation, powered by ⚠ <a target="_blank" href="https://office.com/webapps">Office⚠ </a>.⚠ </iframe>
Neural Networks and Deep Learning Basics:
[PDF] [GoogleSlides] ⚠ <iframe src="https://docs.google.com/presentation/d/e/2PACX-1vQ_qqlIeqDCveAd_q1SIa7PVQPzK5S0jveZPnm3QPVX_tdUK-51PIAEt5QO7qHz6w7LyM3re7-AJ3kF/embed?start=false&loop=false&delayms=3000" frameborder="0" width="1186px" height="691px" allowfullscreen="true" mozallowfullscreen="true" webkitallowfullscreen="true">⚠ </iframe>
Convolutional Neural Networks (CNNs) Basics:
[PDF]
[GoogleSlides]
⚠ <iframe src="https://docs.google.com/presentation/d/e/2PACX-1vRkrt9kmOf4D0TfKoCt5DVzRlVAchCIj2wvuNQTnoNxItsEu-qwZYb3T_Z3_wuomnYd_YtpnPl9fv8X/embed?start=false&loop=false&delayms=1000" frameborder="0" width="1186px" height="691px" allowfullscreen="true" mozallowfullscreen="true" webkitallowfullscreen="true">⚠ </iframe>
Recurrent Neural Networks (RNNs) Basics: [PDF]
[MicrosoftSlides]
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Generative Adversarial Networks (GANs): [PDF]
[MicrosoftSlides]
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