Introduction to Statistical Learning

Study Log

Week 1 (August 2025)

• Day 1: Started reading Chapter 1 - Introduction

  • Covered basic concepts of statistical learning
  • Understood the difference between supervised and unsupervised learning
  • Notes: Focus on prediction vs inference trade-off

• Day 3: Chapter 2 - Statistical Learning

  • Learned about bias-variance tradeoff
  • Covered training vs test error concepts
  • Practice: Worked through conceptual exercises 1-5

Key Concepts Learned

• Supervised Learning: Prediction with known outcomes
• Unsupervised Learning: Finding hidden patterns
• Bias-Variance Tradeoff: Balance between model complexity and generalization
• Cross-Validation: Method for model assessment

Next Steps

• Complete Chapter 2 exercises
• Move on to Chapter 3 - Linear Regression
• Implement examples in Python/R
• Practice with real datasets

Resources

• Main textbook: Introduction to Statistical Learning
• Code examples and labs
• Practice datasets from the book