COURSE DESCRIPTION
Large datasets are increasingly becoming available across many sectors such as healthcare, energy, and online markets. This course focuses on methods that allow “learning” from such datasets to uncover underlying relationships and patterns in the data, with a focus on predictive performance of various models that can be built to represent the underlying function generating the data. The course starts with a review of basic statistical concepts and linear regression. But the course will focus mostly on introducing students to regression, classification, and clustering techniques beyond linear regression, such as tree-based approaches, support vector machines, neural networks/deep learning, LLMs, and unsupervised learning. This course is intended for first- and second-year Masters students. Ph.D. students with an interest machine learning models may also find this course useful.