Course topics and videos

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Introduction to Machine Learning

Modelling for Prediction versus Modelling for Inference

Parametric versus Non-Parametric Methods

Trade-off Between Model Accuracy and Model Interpretability

Supervised versus Unsupervised Learning

Regression versus Classification

Assessing Model Accuracy - Measure of Fit

Bias-Variance Trade-Off

Assessing Model Fit - Classification Setting

Classification Example - K-Nearest Neighbours (kNN)

Confidence Intervals for Coefficient Estimates for Simple Linear Regression Models

Hypothesis Test of Coefficient Estimates for Simple Linear Regression

Accuracy of Coefficient Estimates for Simple Linear Regression

Estimating Simple Linear Regression's Model Coefficients

Introduction to Linear Regression

Accessing Simple Linear Regression Model Accuracy

Residual Standard Error (RSE)

R-Squared Statistic

Multiple Linear Regression

Estimating Multiple Linear Regression Coefficients

Dealing with Qualitative Variables

Including Interaction Terms in the Model (Non-Additive Models)

Including Non-linear Terms in the Model

Problem #1 - Non-linearity of the data

Problem #2 - Correlation of Error Terms

Problem #3 - Non-constant variance of error terms

Problem #4 - Outliers

Problem #5 - High leverage points

Problem #6 - Collinearity

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