This course covers basic concepts and methods in applied probability and stochastic modeling. The intended audience is master’s and doctoral students in programs such as EE, CS, IEOR, Statistics, Mathematics, and those in the DRO division in the Business School. In terms of prerequisites, basic familiarity with probability theory and stochastic processes will be assumed (an ideal preliminary course is IEOR 6711: Stochastic Modeling I, but a more basic substitute will do as well). The topics and material covered in this course complement those covered in IEOR 6712: Stochastic Modeling II, hence the two courses can be taken simultaneously. The exposition will be (mostly) rigorous, yet intentionally skirting some measure-theoretic details; for those interested in such details they can be found in measure theoretic textbooks and other courses (e.g., Probability Theory I/II given in the statistics/math department).

Assaf Zeevi

Kravis Professor of Business

Assaf Zeevi is Professor and holder of the Kravis chair at the Graduate School of Business, Columbia University. His research and teaching interests lie at the intersection of Operations Research, Statistics, and Machine Learning. In particular, he has been developing theory and algorithms for reinforcement learning, Bandit problems, stochastic optimization,  statistical learning and stochastic networks. Application domains include online retail platforms, healthcare...

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