Description:
Supervised learning methods: regression, classification, support vector methods, boosting decision trees, random forest, model selection and assessment: feature engineering, cross-validation methods. Unsupervised learning:-K-means clustering, spectral methods, EM algorithm. Dimensionally reduction and data visualisation techniques, graphical models. Time series analysis. Examples from domain areas like value chains, transport, communication networks and health care.
References:
- T.Hastie, R.Tibshirani and J.Friedman, "Elements of statistical machine learning", Springer,2009.
- S, Shalev-Shwartz and S Ben-David, "Understanding Machine Learning; From Theory to Algorithms"Cambridge University Press 2014.
- M. Mohri, A. Rostamizadeh and Ameet Talwalkar, "Foundation of Machine Learning," The MIT Press 2.
- G. James, D. Witten, T.Hastie and R.Tibshirani, "An Introduction to Statistical Learning," Springer 2013.
- D. Babber "Bayesian Reasoning and Machine Learning," Cambridge University Press 2.
- Abu-Mostafa, Magdon-Ismail and Lin, "Learning from Data, "AMI-Book (Available Online).
- E. Alpaydin, "Introduction to Machine Learning, "MIT Press 2014.
- Kevin P. Murphy, "Machine Learning A Probabilistic Perspective, "4th printing, MIT Press 2014.