BUKD-S 575 Foundations of AI and Machine Learning
- 12 weeks
- 3.00 credits
- Prerequisite(s): BUKD-K520
Artificial Intelligence (AI) is rapidly changing how organizations analyze data, make predictions, and support business decisions. Many AI tools can now perform tasks that once required specialized technical skills. However, when AI is asked to produce forecasts, recommendations, classifications, or other quantitative analyses, it typically relies on fundamental machine learning (ML) methods behind the scenes. Therefore, to use AI effectively, students need to understand not only what AI can produce, but also how to evaluate whether its results are reliable and reproducible, and whether the underlying process is ethical and responsible.
This course introduces students to the basic ideas of AI and ML in business contexts. No prior experience with ML or advanced AI is required. Students will learn how data can be used to generate insights, make predictions, and support strategic decision-making. The course organizes AI and ML knowledge into four layers: applications, tasks, models, and algorithms. We will explore applications across various industries, including banking, retail, online advertising, entertainment, and more. For each application, we will identify the underlying tasks, such as price forecasting, fraud detection, customer behavior prediction, object and facial recognition, and natural language processing. Students will then learn how these tasks can be accomplished using a range of AI and ML models, from simpler approaches to ensemble methods, such as random forests and boosting, as well as neural networks and large language models. Programming is not required, but it is encouraged, especially because modern AI tools have made implementation more accessible to everyone.
The course will also discuss the responsible use of AI, including how to diagnose ML processes and AI reasoning, interpret AI-generated results, recognize limitations, and make informed decisions about when AI outputs should or should not be trusted.