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.
- 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, 2012
- 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, 2012
- Abu-Mostafa, Magdon-Ismail and Lin. “Learning from Data,” AMLBook (available online)
- E. Alpaydin, “Introduction to Machine Learning,” MIT Press, 2014
- Kevin P. Murphy, “Machine Learning: A Probabilistic Perspective,” 4th printing, MIT Press, 2014