Statistical Learning with R

Statistical Learning with R

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Statistical Learning: 1.1 Opening Remarks

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Statistical Learning: 1.1 Opening Remarks

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Statistical Learning with R

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  1. 1 Statistical Learning: 1.1 Opening Remarks
  2. 2 Statistical Learning: 8 Years Later (Second Edition of the Course)
  3. 3 Statistical Learning: 1.2 Examples and Framework
  4. 4 Statistical Learning: 2.1 Introduction to Regression Models
  5. 5 Statistical Learning: 2.2 Dimensionality and Structured Models
  6. 6 Statistical Learning: 2.3 Model Selection and Bias Variance Tradeoff
  7. 7 Statistical Learning: 2.4 Classification
  8. 8 Statistical Learning: 2.R Introduction to R
  9. 9 Statistical Learning: 3.1 Simple linear regression
  10. 10 Statistical Learning: 3.2 Hypothesis Testing and Confidence Intervals
  11. 11 Statistical Learning: 3.3 Multiple Linear Regression
  12. 12 Statistical Learning: 3.4 Some important questions
  13. 13 Statistical Learning: 3.5 Extensions of the Linear Model
  14. 14 Statistical Learning: 3.R Regression in R
  15. 15 Statistical Learning: 4.1 Introduction to Classification Problems
  16. 16 Statistical Learning: 4.2 Logistic Regression
  17. 17 Statistical Learning: 4.3 Multivariate Logistic Regression
  18. 18 Statistical Learning: 4.4 Logistic Regression Case Control Sampling and Multiclass
  19. 19 Statistical Learning: 4.5 Discriminant Analysis
  20. 20 Statistical Learning: 4.6 Gaussian Discriminant Analysis (One Variable)
  21. 21 Statistical Learning: 4.7 Gaussian Discriminant Analysis (Many Variables)
  22. 22 Statistical Learning: 4.8 Generalized Linear Models
  23. 23 Statistical Learning: 4.9 Quadratic Discriminant Analysis and Naive Bayes
  24. 24 Statistical Learning: 4.R.1 Logistic Regression
  25. 25 Statistical Learning: 4.R.2 Linear Discriminant Analysis
  26. 26 Statistical Learning: 4.R.3 Nearest Neighbor Classification
  27. 27 Statistical Learning: 5.1 Cross Validation
  28. 28 Statistical Learning: 5.2 K-fold Cross Validation
  29. 29 Statistical Learning: 5.3 Cross Validation the wrong and right way
  30. 30 Statistical Learning: 5.4 The Bootstrap
  31. 31 Statistical Learning: 5.5 More on the Bootstrap
  32. 32 Statistical Learning: 5.R.1 Cross Validation
  33. 33 Statistical Learning: 5.R.2 Bootstrap
  34. 34 Statistical Learning: 6.1 Introduction and Best Subset Selection
  35. 35 Statistical Learning: 6.2 Stepwise Selection
  36. 36 Statistical Learning: 6.3 Backward stepwise selection
  37. 37 Statistical Learning: 6.4 Estimating test error
  38. 38 Statistical Learning: 6.5 Validation and cross validation
  39. 39 Statistical Learning: 6.6 Shrinkage methods and ridge regression
  40. 40 Statistical Learning: 6.7 The Lasso
  41. 41 Statistical Learning: 6.8 Tuning parameter selection
  42. 42 Statistical Learning: 6.9 Dimension Reduction Methods
  43. 43 Statistical Learning: 6.10 Principal Components Regression and Partial Least Squares
  44. 44 Statistical Learning: 6.R.1 Markdown in RStudio and Best Subset Regression
  45. 45 Statistical Learning: 6.R.2 Forward Stepwise Regression
  46. 46 Statistical Learning: 6.R.3 Model Selection and Cross-Validation
  47. 47 Statistical Learning: 6.R.4 Ridge Regression and Lasso
  48. 48 Statistical Learning: 7.1 Polynomials and Step Functions
  49. 49 Statistical Learning: 7.2 Piecewise Polynomials and Splines
  50. 50 Statistical Learning: 7.3 Smoothing Splines
  51. 51 Statistical Learning: 7.4 Generalized Additive Models and Local Regression
  52. 52 Statistical Learning: 7.R.1 Polynomials in GLMs
  53. 53 Statistical Learning: 7.R.2 Splines and GAMs
  54. 54 Statistical Learning: 8.1 Tree based methods
  55. 55 Statistical Learning: 8.2 More details on Trees
  56. 56 Statistical Learning: 8.3 Classification Trees
  57. 57 Statistical Learning: 8.4 Bagging
  58. 58 Statistical Learning: 8.5 Boosting
  59. 59 Statistical Learning: 8.6 Bayesian Additive Regression Trees
  60. 60 Statistical Learning: 8.R.1 Fitting Trees
  61. 61 Statistical Learning: 8.R.2 Random Forests and Boosting
  62. 62 Statistical Learning: 9.1 Optimal Separating Hyperplane
  63. 63 Statistical Learning: 9.2.Support Vector Classifier
  64. 64 Statistical Learning: 9.3 Feature Expansion and the SVM
  65. 65 Statistical Learning: 9.4 Example and Comparison with Logistic Regression
  66. 66 Statistical Learning: 9.R.1 Support Vector Classifier
  67. 67 Statistical Learning: 9.R.2 Nonlinear Support Vector Machine
  68. 68 Statistical Learning: 10.1 Introduction to Neural Networks
  69. 69 Statistical Learning: 10.2 Convolutional Neural Networks
  70. 70 Statistical Learning: 10.3 Document Classification
  71. 71 Statistical Learning: 10.4 Recurrent Neural Networks
  72. 72 Statistical Learning: 10.5 Time Series Forecasting
  73. 73 Statistical Learning: 10.6 Fitting Neural Networks
  74. 74 Statistical Learning: 10.7 Interpolation and Double Descent
  75. 75 Statistical Learning: 10.R.1 Neural Networks in R and the MNIST data
  76. 76 Statistical Learning: 10.R.2 Convolutional Neural Networks in R
  77. 77 Statistical Learning: 10.R.3 Document Classification
  78. 78 Statistical Learning: 10.R.4 Recurrent Neural Networks
  79. 79 Statistical Learning: 11.1 Introduction to Survival Data and Censoring
  80. 80 Statistical Learning: 11.2 Proportional Hazards Model
  81. 81 Statistical Learning: 11.3 Estimation of Cox Model with Examples
  82. 82 Statistical Learning: 11.4 Model Evaluation and Further Topics
  83. 83 Statistical Learning: 11.R.1 Survival Curves Brain Cancer Data
  84. 84 Statistical Learning: 11.R.2 Cox Models I Publication Data
  85. 85 Statistical Learning: 11.R.3 Cox Models II Call Center Data
  86. 86 Statistical Learning: 12.1 Principal Components
  87. 87 Statistical Learning: 12.2 Higher order principal components
  88. 88 Statistical Learning: 12.3 k means Clustering
  89. 89 Statistical Learning: 12.4 Hierarchical Clustering
  90. 90 Statistical Learning: 12.5 Matrix Completion
  91. 91 Statistical Learning: 12.6 Breast Cancer Example
  92. 92 Statistical Learning: 12.R.1 Principal Components
  93. 93 Statistical Learning: 12.R.2 K means Clustering
  94. 94 Statistical Learning: 12.R.3 Hierarchical Clustering
  95. 95 Statistical Learning: 13.1 Introduction to Hypothesis Testing
  96. 96 Statistical Learning: 13.1 Introduction to Hypothesis Testing II
  97. 97 Statistical Learning: 13.2 Introduction to Multiple Testing and Family Wise Error Rate
  98. 98 Statistical Learning: 13.3 Bonferroni Method for Controlling FWER
  99. 99 Statistical Learning: 13.4 Holm's Method for Controlling FWER
  100. 100 Statistical Learning: 13.5 False Discovery Rate and Benjamini Hochberg Method
  101. 101 Statistical Learning: 13.6 Resampling Approaches
  102. 102 Statistical Learning: 13.6 Resampling Approaches II
  103. 103 Statistical Learning: 13.R.1 Bonferroni and Holm
  104. 104 Statistical Learning: 13.R.1 Bonferroni and Holm II

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