WEEK 7: LINEAR REGRESSION

Supervised learning, linear regression, gradient descent, and polynomial regression

October 27, 2025

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Weekly Materials

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Lecture Slides

Professor's main lecture presentation

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TA Practice Slides

Hands-on tutorials and practice exercises

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Lesson Guide

Comprehensive explanations and theory

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Code Examples

Ridge Regression Example Gradient Descent Implementation K-Fold Cross Validation Stock Prediction Notebook

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References & Resources

ISL Chapter 3 Linear Regression

PML Ch. 6.3–6.5 Bayesian linear regression, uncertainty, model comparison

Additional Notes

Week 7: Linear Regression

Topics Covered

• Supervised Learning - the general idea
• Linear Regression (with multiple variables)
• Gradient Descent
• Polynomial Regression
• Tuning Model Complexity
• Stock Market Prediction (if time permits)
• Introduction to Pandas (quick tour; self-study)

Code Examples

This week includes practical implementations of regression algorithms and real-world applications like stock market prediction.

Further Reading

• ISL Chapter 3: Linear Regression
• PML Ch. 6.3–6.5: Bayesian linear regression, uncertainty, model comparison