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. Dimensionality reduction and data visualization 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,20092

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 20148.Kevin P.Murphy,"Machine Learning A Probabilistic Perspective, "4th printing, MIT Press 2014