## Topic outline

• ### General

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• ### Introduction to Statistical Learning

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Modelling for prediction versus modelling for interference Page
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Parametric versus Non Parametric Methods Page
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Model Prediction Accuracy and Model Interpretability Trade Off Page
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Supervised versus Unsupervised Learning Page
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Regression versus Classification Page
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Assessing Model Accuracy - Measury of Quality of Fit Page
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Assessing Model Accuracy Classification Setting Page
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Classification Example K Nearest Neighbours KNN Page
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Confidence Intervals for Coefficient Estimates: Simple Linear Regression Page
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Estimating Model Coefficients: Simple Linear Regression Page
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Hypothesis Test of Coefficient Estimates: Simple Linear Regression Page
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Accuracy of Coefficient Estimates: Simple Linear Regression Page
• ### Linear Regression

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Introduction to Linear Regression Page
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Assessing Simple Linear Model Accuracy Page
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Residual Standard Error (RSE) Page
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R- Squared Statistic Page
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Multiple Linear Regression Page
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Estimating Multiple Linear Regression Coefficients Page
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Question 1 - Is There a Relationship Between Response and Predictors Page
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Question 2 - Variable Selection Page
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Question 3 - Model Accuracy Page
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Dealing with Qualitative Variables Page
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Including Interaction Terms in the Model (Non additive Linear Models) Page
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Including Non Linear Terms in Linear Models Page
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Problem #1 - Non linearity of the Data Page
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Problem #2 - Correlation of the Error Terms Page
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Problem #3 - Nonconstant Variance of Error Terms Page
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Problem #4 - Outliers Page
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Problem #5 - High Leverage Points Page
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Problem #6 - Collinearity Page
• ### Classification

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Introduction to Classification Page
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Why Linear Regression Will Not Work Perfectly In Classification Page
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Introduction to Logistic Regression Page
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The Logistic Model Page
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Estimating Logistic Regression Coefficients Maximum Likelihood Method Page
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Making Predictions with Logistic Regression Page
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Multiple Logistic Regression Page
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Introduction to Linear Discriminant Analysis (LDA) Page
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Bayes' Theorem of Classification Page
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Linear Discriminant Analysis One Predictor Clean Page
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Linear Discriminant Analysis with More Predictors Page
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The Confusion Matrix, Sensitity and Specificity Page
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The ROC Curve Page
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• ### Validation and The Bootstrap Methods

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Introduction to Resampling Methods Page
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Method 1 - Validation Set Approach Page
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Method 2 - Leave One Out Cross Validation Page
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Method 3 - k-Fold Cross Validation Page
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Cross Validation for Classification Settings Page
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The Bootstrap Method Page
• ### Linear Model Selection and Regularization

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Introduction to Model Selection and Regularization Page
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Best Subset Selection Method Page
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Forward Stepwise Selection Method Page
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Backward Stepwise Selection Method Page
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Model Selection Choosing the Optimal Model Page
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Cp Estimate for Test Error Page
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Akaike Information Criterion (AIC) Estimate for Test Error Page
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Bayesian Information Criterion (BIC) Estimate for Test Error Page
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Adjusted R-Squared Estimate for Test Error Page
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Validation Set and Cross Validation Estimates for Test Error Page
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Introduction to Shrinkage Methods Page
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Introduction to Ridge Regression Page
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Ridge Regression Example Page
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The Lasso Page
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The Lasso Example Page
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Lasso & Ridge Rigression using Mathematical Optimisation Page
• ### Dimension Reduction Methods

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Introduction to Dimension Reduction Methods Page
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Principal Component Analysis Page
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Principal Component Analysis Example Page
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Scree Plots and How PCA Actually Reduces Dimension Page
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Principal Component Regression Page
• ### Tree Based Methods

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Introduction to Tree Based Methods Page