Lesson 3: Clustering, Feature Engineering, and Dimensionality Reduction
Lesson Overview
Develop practical capability in clustering, feature engineering, and dimensionality reduction as part of the Machine Learning with MATLAB learning path.
Topics Covered
K-means and hierarchical clustering
Anomaly-detection concepts
Feature extraction and selection
Principal component analysis
Handling imbalanced classes
Pipeline design for repeatable preprocessing
Practical Activity
Build a guided MATLAB exercise covering k-means and hierarchical clustering.
Apply anomaly-detection concepts to a practical engineering or data scenario.
Validate results and troubleshoot an issue involving pipeline design for repeatable preprocessing.
Key Takeaways
Explain and apply k-means and hierarchical clustering.
Use feature extraction and selection in a structured workflow.
Validate outputs with plots, metrics, tests, or simulation evidence.
