9.520/6.860: Statistical Learning Theory and Applications

Wikipedia entries

created or edited as part of course projects (2012 - 2015)

  1. Autoencoder [wiki]
  2. Bayesian interpretation of regularization [wiki]
  3. Convolutional neural network [wiki]
  4. Generalization error [wiki]
  5. Deep learning [wiki]
  6. Diffusion map [wiki]
  7. Distribution learning theory [wiki]
  8. Early stopping and regularization [wiki]
  9. Error tolerance (PAC) [wiki]
  10. Feature Learning [wiki]
  11. Hyper basis function network [wiki]
  12. Kernel embedding of distributions [wiki]
  13. Kernel methods for vector output [wiki]
  14. Lasso (statistics) [wiki]
  15. Learnable function class [wiki]
  16. Loss function for classification [wiki]
  17. Low-rank matrix approximations [wiki]
  18. M-theory (Learning Framework) [wiki]
  19. Manifold regularization [wiki]
  20. Matrix completion [wiki]
  21. Matrix regularization [wiki]
  22. Multiple instance learning [wiki]
  23. Multiple kernel learning [wiki]
  24. Occam learning (PAC Learning) [wiki]
  25. Online machine learning [wiki]
  26. Positive-definite kernel [wiki]
  27. Principal component regression [wiki]
  28. Proximal gradient methods for learning [wiki]
  29. Regularization (Mathematics) [wiki]
  30. Regularization by spectral filtering [wiki]
  31. Regularization perspectives on support vector machines [wiki]
  32. Regularized least squares [wiki]
  33. Representer theorem [wiki]
  34. Reproducing kernel Hilbert space [wiki]
  35. Sample complexity [wiki]
  36. Semi-supervised learning [wiki]
  37. Sparse dictionary learning [wiki]
  38. Sparse PCA [wiki]
  39. Statistical learning theory [wiki]
  40. Structured sparsity regularization [wiki]
  41. Support vector machine [wiki]
  42. Vapnik-Chervonenkis theory [wiki]