Syllabus
Introduction to data mining concepts; linear methods for regression; classification methods: k- nearest neighbourclassifiers, decision tree, logistic regression, naive Bayes, Gaussian discriminant analysis; model evaluation & selection;unsupervised learning: association rules; apriori algorithm, FP tree, cluster analysis, self organizing maps, google page ranking; dimensionality reduction methods: supervised feature selection, principal component analysis; ensemble learning: bagging, boosting, AdaBoost; outlier mining; imbalance problem; multi class classification; evolutionary computation; introduction to semi supervised learning, transfer learning, active learning, datawarehousing.
Text Books
Same as Reference
References
- Bishop, C.M., Pattern Recognition and Machine Learning, Springer (2006).
- Hastie, T., Tibshirani, R., and Friedman, J., The Elements of Statistical Learning: Data Mining, Inference,and Prediction, Springer(2002).
- Han, J., Kamber, M., and Pei, J., Data Mining: Concepts and Techniques, 3rd ed., Morgan Kaufmann (2012).
- Mitchell, T. M., Machine Learning, McGraw-Hill (1997).
Course Outcomes (COs):
CO1: Develop a solid understanding of the fundamental concepts and principles of both machine learning and data mining.
CO2: Explore how machine learning and data mining contribute to knowledge discovery.
CO3:Cultivate critical thinking skills by analyzing and interpreting the results of machine learning and data mining algorithms in various contexts.