Course Description: This course teaches practical data mining and big-data analytics—covering supervised/unsupervised learning, association rules, anomaly detection, and feature engineering—across text, image, and audio domains using real-world datasets. Students build end-to-end mini projects with Python (scikit-learn, PyTorch) and WEKA, and learn evaluation with ROC/PR, AUC, and explainability.
- Detailed Course Description
- Lecture Notes
- Homeworks
- Project
- Exams and Sample Questions