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regularization

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  1. Stanford CS229M - Lecture 16: Implicit regularization in classification problemsStanford CS229M: Machine Learning Theory - Fall 2021Notes
  2. Stanford CS229M - Lecture 15: Implicit regularization effect of initializationStanford CS229M: Machine Learning Theory - Fall 2021Notes
  3. Stanford CS229M - Lecture 14: Neural Tangent Kernel, Implicit regularization of gradient descentStanford CS229M: Machine Learning Theory - Fall 2021Notes
  4. Stanford CS229M - Lecture 13: Neural Tangent KernelStanford CS229M: Machine Learning Theory - Fall 2021Notes
  5. Stanford CS229M - Lecture 11: All-layer marginStanford CS229M: Machine Learning Theory - Fall 2021Notes
  6. Stanford CS229M - Lecture 10: Generalization bounds for deep netsStanford CS229M: Machine Learning Theory - Fall 2021Notes
  7. Stanford CS229M - Lecture 9: Covering number approach, Dudley TheoremStanford CS229M: Machine Learning Theory - Fall 2021Notes
  8. Stanford CS229M - Lecture 8: Refined generalization bounds for neural nets, Kernel methodsStanford CS229M: Machine Learning Theory - Fall 2021Notes
  9. Stanford CS229M - Lecture 7: Challenges in DL theory, generalization bounds for neural netsStanford CS229M: Machine Learning Theory - Fall 2021Notes
  10. Stanford CS229M - Lecture 6: Margin theory and Rademacher complexity for linear modelsStanford CS229M: Machine Learning Theory - Fall 2021Notes
  11. Stanford CS229M - Lecture 5: Rademacher complexity, empirical Rademacher complexityStanford CS229M: Machine Learning Theory - Fall 2021Notes
  12. Stanford CS229M - Lecture 4: Advanced concentration inequalitiesStanford CS229M: Machine Learning Theory - Fall 2021Notes
  13. Stanford CS229M - Lecture 3: Finite hypothesis class, discretizing infinite hypothesis spaceStanford CS229M: Machine Learning Theory - Fall 2021Notes
  14. Stanford CS229M - Lecture 2: Asymptotic analysis, uniform convergence, Hoeffding inequalityStanford CS229M: Machine Learning Theory - Fall 2021Notes
  15. Stanford CS229M - Lecture 1: Overview, supervised learning, empirical risk minimizationStanford CS229M: Machine Learning Theory - Fall 2021Notes
  16. Class 8 - Deep learningRegularization Methods for Machine Learning 2016Notes
  17. Class 7 - Dictionary learningRegularization Methods for Machine Learning 2016Notes
  18. Class 6 - Structured sparsityRegularization Methods for Machine Learning 2016Notes
  19. Class 5 - Sparsity based regularizationRegularization Methods for Machine Learning 2016Notes
  20. Class 4 - Regularization for multi-task learningRegularization Methods for Machine Learning 2016Notes
  21. Class 3 - Early Stopping and Spectral RegularizationRegularization Methods for Machine Learning 2016Notes
  22. Class 2 - Tikhonov regularization and kernelsRegularization Methods for Machine Learning 2016Notes
  23. Class 1 - Statistical Learning TheoryRegularization Methods for Machine Learning 2016Notes
  24. 9.520 - 10/28/2015 - Class 15 - Charlie Frogner: Stability of Tikhonov RegularizationMIT Course 9.520 - Statistical Learning Theory and Applications, Fall 2015Notes
  25. 9.520 - 10/26/2015 - Class 14 - Charlie Frogner: Generalization Bounds, Intro to StabilityMIT Course 9.520 - Statistical Learning Theory and Applications, Fall 2015Notes
  26. 9.520 - 10/21/2015 - Class 13 - Prof. Lorenzo Rosasco: Multiple Kernel LearningMIT Course 9.520 - Statistical Learning Theory and Applications, Fall 2015Notes
  27. 9.520 - 10/19/2015 - Class 12 - Prof. Lorenzo Rosasco: Structured Sparsity RegularizationMIT Course 9.520 - Statistical Learning Theory and Applications, Fall 2015Notes
  28. 9.520 - 10/14/2015 - Class 11 - Prof. Lorenzo Rosasco: Proximal MethodsMIT Course 9.520 - Statistical Learning Theory and Applications, Fall 2015Notes
  29. 9.520 - 10/13/2015 - Class 10 - Prof. Lorenzo Rosasco: Sparsity Based RegularizationMIT Course 9.520 - Statistical Learning Theory and Applications, Fall 2015Notes
  30. 9.520 - 09/14/2015 - Class 02 - Prof. Tomaso Poggio: The Learning Problem and RegularizationMIT Course 9.520 - Statistical Learning Theory and Applications, Fall 2015Notes
  31. 9.520 - 09/16/2015 - Class 03 - Carlo Ciliberto & Charlie Frogner: Math CampMIT Course 9.520 - Statistical Learning Theory and Applications, Fall 2015Notes
  32. 9.520 - 09/09/2015 - Class 01 - Prof. Tomaso Poggio: The Course at a GlanceMIT Course 9.520 - Statistical Learning Theory and Applications, Fall 2015Notes
  33. 9.520 - 10/07/2015 - Class 09 - Prof. Lorenzo Rosasco: Iterative Regularization via Early StoppingMIT Course 9.520 - Statistical Learning Theory and Applications, Fall 2015Notes
  34. 9.520 - 9/30/2015 - Class 07 - Prof. Lorenzo Rosasco: Logistic Regression and Support ...MIT Course 9.520 - Statistical Learning Theory and Applications, Fall 2015Notes
  35. 9.520 - 10/05/2015 - Class 08 - Prof. Lorenzo Rosasco: Regularized Least SquaresMIT Course 9.520 - Statistical Learning Theory and Applications, Fall 2015Notes
  36. 9.520 - 9/28/2015 - Class 06 - Prof. Lorenzo Rosasco - Tikhonov Regularization and the ...MIT Course 9.520 - Statistical Learning Theory and Applications, Fall 2015Notes
  37. 9.520 - 9/23/2015 - Class 05 - Prof. Lorenzo Rosasco - Dictionaries, Feature Maps and Mercer TheoremMIT Course 9.520 - Statistical Learning Theory and Applications, Fall 2015Notes
  38. 9.520 - 9/21/2015 - Class 04 - Prof. Lorenzo Rosasco: Reproducing Kernel Hilbert SpacesMIT Course 9.520 - Statistical Learning Theory and Applications, Fall 2015Notes

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