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Class 01 - The Course at a Glance
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Statistical Learning Theory and Applications
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- 1 Class 01 - The Course at a Glance
- 2 Math Camp for 9.520/6.860S Statistical Learning Theory and Applications
- 3 Class 02 - The Learning Problem and Regularization
- 4 Class 03 - Reproducing Kernel Hilbert Spaces
- 5 Class 04 - Positive Definite Functions and Feature Maps
- 6 Class 05 - Feature Maps (cont.), Tikhonov Regularization and the Representer Theorem
- 7 Class 06 - Logistic Regression and Support Vector Machines
- 8 Class 07 - Regularized Least Squares
- 9 Class 08 - Iterative Regularization via Early Stopping
- 10 Class 09 - Learning with Stochastic Gradients
- 11 Class 10 - Large Scale Kernel Methods
- 12 Class 11 - Sparsity Based Regularization
- 13 Class 12 - Convex Relaxation and Proximal Gradient
- 14 Class 13 - Structured Sparsity Regularization
- 15 Class 14 - Multiple Kernel Learning
- 16 Class 15 - Learning Theory
- 17 Class 16 - Generalization Error and Stability
- 18 Class 23 - Deep Learning Theory: Optimization
