Syllabus
Introduction to data mining concepts; linear methods for regression; classification methods: k- nearest neighbor classifiers, decision tree, logistic regression, naive Bayes, Gaussian discriminant analysis; model evaluation & selection; unsupervised learning: association rules; apriority algorithm, FP tree, cluster analysis, self- organizing maps, google page ranking; dimensionality reduction methods: supervised featureselection, principal component analysis; ensemble learning: bagging, boosting, Ada Boost; outlier mining; imbalance problem; multi class classification; evolutionary computation; introduction to semi supervised learning, transfer learning, active learning, data warehousing.
Text Books
Same as Reference
References
1. Pattern Recognition and Machine Learning, Bishop, C. M., Springer, 2006.
2. The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Hastie, T., Tibshirani, R., and Friedman, J., Springer, 2002.
3. Data Mining: Concepts and Techniques, Han, J., Kamber, M., and Pei, J., 3rd ed., Morgan Kaufmann, 2012.
4. Machine Learning, Mitchell, T. M., 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.