Unit 3 · Model Evaluation and Selection

# Syllabus

• Evaluating Machine Learning algorithms and Model Selection, Introduction to Statistical Learning Theory, Ensemble Methods (Boosting, Bagging, Random Forests)

# Topics

• 3.1Confusion Matrix & Classification Metrics★★★
• 3.2ROC, AUC & Other Evaluation Metrics☆
• 3.3Overfitting, Underfitting & the Bias–Variance Trade-off★★★
• 3.4Model Selection & Cross-Validation★★
• 3.5Introduction to Statistical Learning Theory (PAC, VC Dimension)★
• 3.6Ensemble Learning: Principle & Types★★★
• 3.7Bagging & Bootstrapping★★★
• 3.8Boosting & Bagging vs Boosting★★★
• 3.9Random Forest★★