Unit 4: Advanced Topics
Overview
This unit covers advanced machine learning techniques including deep learning, ensemble methods, and advanced feature engineering.
Key Topics:
- Neural networks and deep learning
- Convolutional Neural Networks (CNN)
- Recurrent Neural Networks (RNN)
- Ensemble methods (Bagging, Boosting, Stacking)
- Advanced feature engineering
- Model interpretability
Learning Outcomes:
- Build and train neural networks
- Apply ensemble methods for improved performance
- Understand deep learning architectures
- Engineer advanced features
- Interpret complex models
- Optimize model performance
📖 Lecture Content
Lecture 4.1: Neural Networks Basics
- Artificial Neural Networks (ANN)
- Neurons and activation functions
- Forward propagation and backpropagation
- Training neural networks
- TensorFlow and Keras
Lecture 4.2: Deep Learning Architectures
- Convolutional Neural Networks (CNN)
- Recurrent Neural Networks (RNN)
- LSTM and GRU
- Transfer learning
- Pre-trained models
Lecture 4.3: Ensemble Methods
- Bagging and Random Forest
- Boosting (AdaBoost, Gradient Boosting)
- XGBoost and LightGBM
- Stacking and Voting
- Blending techniques
Lecture 4.4: Advanced Topics
- Feature engineering and selection
- Feature importance
- Model interpretability (SHAP, LIME)
- Regularization techniques
- Optimization algorithms
🧪 Associated Practicals
- Practical 9: Support Vector Machines (SVM)
- Practical 10: Model Comparison & Selection
- Practical 12: Neural Networks Basics
- Practical 13: Ensemble Methods Advanced
✅ Study Checklist
- Build neural networks with TensorFlow/Keras
- Train deep learning models
- Apply CNN for image classification
- Use RNN for sequential data
- Implement ensemble methods
- Compare multiple algorithms
- Interpret model predictions
- Optimize hyperparameters
📚 Key Techniques
| Technique | Purpose | Use Cases |
|---|---|---|
| Neural Networks | Learn complex patterns | General purpose |
| CNN | Process image data | Image classification, detection |
| RNN | Process sequential data | Time series, NLP |
| Random Forest | Ensemble bagging | Classification, regression |
| Gradient Boosting | Sequential ensemble | High-performance prediction |
| Stacking | Combine multiple models | Kaggle competitions |
💾 Resources
- Deep Learning Guide
- Ensemble Methods Reference
- Feature Engineering Handbook
- Model Interpretability Guide
📝 Assessment
- Weekly Test 4 (WT4) - Unit 4 concepts
- Class Test 2 (CT2) - Units 3-4
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