Unit 2: Supervised Learning
Overview
This unit focuses on supervised learning algorithms where models learn from labeled data. Students will master regression and classification techniques.
Key Topics:
- Regression algorithms (Linear, Polynomial, Regularized)
- Classification algorithms (Logistic Regression, Decision Trees, SVM, KNN)
- Model evaluation and validation
- Hyperparameter tuning
- Real-world applications
Learning Outcomes:
- Build and evaluate regression and classification models
- Choose appropriate algorithms for different problems
- Validate models using proper techniques
- Optimize model performance through hyperparameter tuning
- Apply supervised learning to real-world datasets
📖 Lecture Content
Lecture 2.1: Regression Algorithms
- Simple and Multiple Linear Regression
- Polynomial regression
- Regularization (Ridge, Lasso, ElasticNet)
- Evaluation metrics (MSE, RMSE, R²)
Lecture 2.2: Classification Algorithms
- Logistic Regression
- Decision Trees
- Support Vector Machines (SVM)
- K-Nearest Neighbors (KNN)
- Naive Bayes
Lecture 2.3: Model Evaluation & Validation
- Train-test split and cross-validation
- Confusion matrix and classification metrics
- Precision, Recall, F1-Score
- ROC curves and AUC
- Overfitting and underfitting
Lecture 2.4: Hyperparameter Tuning
- Grid Search and Random Search
- Bayesian Optimization
- Early stopping
- Learning curves
🧪 Associated Practicals
- Practical 3: Linear Regression Implementation
- Practical 4: Classification with Logistic Regression
- Practical 5: Decision Trees & Random Forest
✅ Study Checklist
- Build and train regression models
- Implement classification algorithms
- Evaluate model performance correctly
- Apply cross-validation techniques
- Interpret confusion matrices and ROC curves
- Tune hyperparameters for better performance
- Handle class imbalance issues
📚 Key Algorithms
| Algorithm | Type | Use Case | Strengths |
|---|---|---|---|
| Linear Regression | Regression | Continuous prediction | Simple, interpretable |
| Logistic Regression | Classification | Binary classification | Fast, probability output |
| Decision Tree | Classification | Non-linear patterns | Interpretable, handles non-linear |
| SVM | Classification | High-dimensional data | Effective in high dimensions |
| KNN | Classification | Instance-based | Simple, non-parametric |
💾 Resources
📝 Assessment
- Weekly Test 2 (WT2) - Unit 2 fundamentals
- Class Test 1 (CT1) - Units 1-2 comprehensive
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