Statistical Learning Introduction-Introduction
Earn Academic Credit for your Work!
Course Support
Statistical Learning Introduction-Setting the Foundation
Introduction and Welcome
()
Supervised vs. Unsupervised
()
Notation Overview
()
Overview Example & Discussion
()
Statistical Learning Introduction-General Concepts
Prediction
()
Inference
()
Parametric Methods
()
Interpretability vs. Flexibility
()
Quantitative vs. Qualitative
()
Accuracy-Accuracy
Model Accuracy
()
Bias-Variance Trade-off
()
Assessing Accuracy — Classification
()
Accuracy-Bayes Classifier
Bayes Classifier Part I
()
Bayes Classifier Part II
()
Assessing Accuracy — KNN
()
Simple Linear Regression-Coefficients
Simple Linear Regression Overview
()
Coefficient Estimation
()
Accuracy of Coefficient Estimates
()
Simple Linear Regression-Concepts
Model Accuracy
()
Correlation
()
Multiple Linear Regression-Multiple Linear Regression
Multiple Linear Regression Overview
()
Relationship Between X and Y
()
Multiple Linear Regression-Predictors
Qualitative Predictors
()
Interaction Terms
()
Multiple Linear Regression-Model Specifics
Multicollinearity
()
Linear Regression vs. KNN Regression
()
Classification Overview-Logistic Regression
Classification Overview
()
Linear vs. Logistics Regression
()
Logistic Regression
()
Classification Overview-Logistic Regression Part II
Estimating Coefficients
()
Multiple Logistic Regression
()
Classification Overview-Generative Models
Generative Models Part I
()
Generative Models Part II
()
Classification Models-LDA
LDA
()
LDA Estimates
()
LDA with p > 1
()
Standard to Multivariate Details
()
Classification Models-QDA & Naive Bayes
QDA
()
Naive Bayes
()
Classification Models-Poisson Regression, Link Functions & Conclusion
Poisson Regression
()
Link Functions and Conclusion
()