Regression and Classification

Module 1: Statistical Learning Introduction

• Explore the basics of statistical learning and understand the distinctions between supervised and unsupervised techniques.
• Learn about notation, prediction, inference, parametric methods, and the trade-offs between interpretability and flexibility.
• Discover the academic credit opportunities and available course support.

Module 2: Accuracy

• Delve into model accuracy and the bias-variance trade-off, and learn how to assess accuracy in classification and KNN.
• Understand the analogy between training error rate and testing error rate.

Module 3: Simple Linear Regression

• Gain insights into coefficient estimation, accuracy of coefficient estimates, and correlation in simple linear regression.
• Tackle the challenges related to correlation problems.

Module 4: Multiple Linear Regression

• Explore the relationship between X and Y, qualitative predictors, interaction terms, and multicollinearity in multiple linear regression.
• Understand the differences between linear regression and KNN regression.

Module 5: Classification Overview

• Learn about the overview of classification, linear vs. logistic regression, logistic regression, and estimating coefficients.
• Explore multiple logistic regression and generative models.

Module 6: Classification Models

• Discover LDA, QDA, naive Bayes, and Poisson regression in the context of classification.
• Get insights into link functions and the use of linear regression in classification.