Lecture 3: Structured Data Processing (Part I)

(Last updated: Jan 27, 2026)

This lecture explains the theory of Decision Tree and Random Forest models that are used in the structured data processing module.

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Preparation

Read the required course readings.

Lecture

Below are the slides:

• Slides for Lecture 3: Structured Data Processing (Decision Tree, Random Forest, and PCA)

Required Course Readings

• Section 3.1, 3.2, 3.3, and 3.4 (including 3.4.1 and 3.4.2) about decision tree learning in book Machine Learning (Mitchell, 1997)
• Section 8.2.1 (Bagging) and 8.2.2 (Random Forests) in book An Introduction to Statistical Learning (James et al., 2013)

Optional Course Readings

• Section 5.4 (Estimators, Bias and Variance) in book Deep Learning (Goodfellow et al., 2016).
• Section 2.2.2 (The Bias-Variance Trade-Off) and 12.2 (Principal Components Analysis, including 12.2.1, 12.2.2 ) in book An Introduction to Statistical Learning (James et al., 2013)

Additional Resources

Below are videos from StatQuest that explains the Decision Tree model and PCA nicely:

• Decision and Classification Trees, Clearly Explained
• Principal Component Analysis (PCA), Step-by-Step