ThisrepositorycontainsJupyternotebooksimplementingthealgorithmsfoundinthebookandsummaryofthetextbook.
Requirementsjupyterpandasnumpymatplotlibscipytensorflow2-temporarilyuntilIhavealotoffreetimetoimplementthemfromscratchanditisusedonlyinChapter11.TableofContentsChapter2
2.3LeastSquaresandNearestNeighbors(nbviewer)2.4StatisticalDecisionTheory(nbviewer)2.5LocalMethodsinHighDimensions(nbviewer)2.6StatisticalModels,SupervisedLearningandFunctionApproximation(nbviewer)2.7StructuredRegressionModels(nbviewer)2.8ClassesofRestrictedEstimators(nbviewer)2.9ModelSelectionandtheBias-VarianceTradeoff(nbviewer)Chapter3
3.1Introduction(nbviewer)3.2LinearRegressionModelsandLeastSquares(nbviewer)3.2.1ExampleProstateCancer(nbviewer)3.2.2TheGauss–MarkovTheorem(nbviewer)3.2.3MultipleRegressionFromSimpleUnivariateRegression(nbviewer)3.2.4MultipleOutputs(nbviewer)3.3SubsetSelection(nbviewer)3.4ShrinkageMethods(nbviewer)3.4.1RidgeRegression(nbviewer)3.4.2TheLasso(nbviewer)TODO:3.4.3Discussion:SubsetSelection,RidgeRegressionandtheLasso(nbviewer)3.4.4LeastAngleRegression(nbviewer)3.5MethodsUsingDerivedInputDirections(nbviewer)Chapter4
4.1Introduction(nbviewer)4.2LinearRegressionofanIndicatorMatrix(nbviewer)4.3LinearDiscriminantAnalysis(nbviewer)4.3.1RegularizedDiscriminantAnalysisnbviewer)4.3.2ComputationsforLDA(nbviewer)4.3.3Reduced-RankLinearDiscriminantAnalysis(nbviewer)4.4LogisticRegression(nbviewer)4.4.1FittingLogisticRegressionModels(nbviewer)4.4.2Example:SouthAfricanHeartDisease(nbviewer)4.4.3QuadraticApproximationsandInference(nbviewer)4.4.4L1RegularizedLogisticRegression(nbviewer)Chapter11
WIP:11.7Example:ZIPCodeData(nbviewer)
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