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
1. Bishop, C.M., Pattern Recognition and Machine Learning, Springer (2006).
2. Hastie, T., Tibshirani, R., and Friedman, J., The Elements of Statistical Learning: Data Mining, Inference,and Prediction, Springer(2002).
3. Han, J., Kamber, M., and Pei, J., Data Mining: Concepts and Techniques, 3rd ed., Morgan Kaufmann (2012).
4. 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.