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IE 615: Data Analytics in Operations Research

Prerequisite: Basic course in probability/statistics and optimization



Data Analytics has emerged as an essential tool in solving many Operations Research problems. This course aims to teach various learning algorithms and help gain hands-on experience working with many industry-driven problems.



  • 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.



  1. T. Hastie, R. Tibshirani and J. Friedman, “Elements of statistical machine learning,” Springer, 2009
  2. S. Shalev-Shwartz and S. Ben-David, “Understanding Machine Learning: From Theory to Algorithms,” Cambridge University Press, 2014
  3. M. Mohri, A. Rostamizadeh, and Ameet Talwalkar, “Foundation of Machine Learning,” The MIT Press, 2012
  4. G. James, D. Witten, T. Hastie, and R. Tibshirani, “An Introduction to Statistical Learning,” Springer, 2013
  5. D. Babber “Bayesian Reasoning and Machine Learning,” Cambridge University Press, 2012
  6. Abu-Mostafa, Magdon-Ismail and Lin. “Learning from Data,” AMLBook (available online)
  7. E. Alpaydin, “Introduction to Machine Learning,” MIT Press, 2014
  8. Kevin P. Murphy, “Machine Learning: A Probabilistic Perspective,” 4th printing, MIT Press, 2014