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Course Content


Topic outline

  • Introduction to Statistical Learning

    • 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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    • Bias Variance Trade Off 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
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  • Linear Regression

    • 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
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  • Classification

    • 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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    • Quadratic Discriminant Analysis Page
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  • Validation and The Bootstrap Methods

    • 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
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  • Linear Model Selection and Regularization

    • 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
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  • Dimension Reduction Methods

    • 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
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  • Tree Based Methods

    • Introduction to Tree Based Methods Page
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    • Regression Decision Trees Page
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    • Predicting Using a Regression Decision Tree Page
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    • Pruning Decision Trees Page
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