Big Data and Machine Learning for Leaders II

Catalog description

Machine learning relies on interdisciplinary techniques from statistics, linear algebra, and optimization to detect structure in large volumes of data and solve prediction problems. Students will gain a theoretical understanding of why the algorithms work when they fail, and how they create value. They will also gain hands-on experience training machine learning models in R, deriving insights, and making predictions from real-world data. Main topics include autoregressive and moving average models, seasonality, long memory ARMA, reinforcement learning, and ensemble methods. The course will also explore applications of the learning algorithms to industry-specific questions in finance, marketing, and operations. Prerequisite(s): IDS 5420 and IDS 6420 .