Applied Machine Learning for Business Analytics

Catalog description

This course provides students with an overview of machine learning techniques, focusing on practical applications in business analytics. Students begin with the basics of data exploration and data preparation. Using practical examples and hands-on learning, the students will then engage in model selection, training, model assessment, and validation to solve problems in a range of business domains. The course covers a range of techniques including linear regression, logistic regression, adaptive boosting, decision trees, random forests, K- Nearest neighbors, and support vector machines. It also introduces students to the basics of contemporary model architectures (e.g., neural network and generative AI). The course aims to equip students with both theoretical knowledge and practical skills essential for addressing real-world challenges in business analytics.