STAT4650

Description

Machine Learning, as a key branch of Artificial Intelligence (AI), enables computers and machines to learn autonomously, perform tasks, and improve their performance through experience and exposure to more data. This course, together with Intermediary Statistical Modeling for Analytics, provides a comprehensive overview of techniques drawn from the fields of machine learning, data mining, and statistics. The goal of these two courses is to prepare students with an intellectual framework for effective problem-solving. This course focuses on the application of statistical modeling and scenario optimization to enhance business decision-making. As such, this course is essentially a data science course with an emphasis on statistical methodologies. At the same time, it highlights the practical aspects of business analytics by embedding these methodologies in applications and by underlining the general objective of improving the speed, reliability, and quality of decisions. Topics covered include multiple linear regression, logistic regression, cross-validation, model selection, dimension reduction, nonlinear methods, tree-based methods, clustering techniques, and an introduction to neural networks. Support vector machines and time series forecasting are also discussed, if time permits. Real-life datasets will be used for illustrations, with Python and R serving as the primary tools for answering business questions.