Fundamentals of Prescriptive AI and Decision-Making
Learn how to turn data into better decisions using prescriptive AI, combining machine learning and optimization to solve real-world challenges.
Course Information
Certificate Track
About this Course
Go beyond predicting outcomes and learn how to make smarter decisions with prescriptive AI. This course helps you understand how data, models, and optimization work together to recommend the best course of action in complex, real-world situations. You’ll build a clear understanding of how decisions are designed, tested, and improved using data.
You’ll explore both data-driven and model-driven approaches, from using machine learning to create decision policies to applying optimization techniques that handle real-world constraints. Along the way, you’ll learn how to test scenarios, understand trade-offs, and design systems that scale. Real-world examples in areas like logistics, resource planning, and revenue management help bring these ideas to life.
Fundamentals of Prescriptive AI and Decision-Making is one of the five courses in Universal AI, a self-paced program designed to help you go from beginner to AI authority—no coding or technical skills required.
What you'll learn
- Understand the difference between predicting outcomes and making decisions with data
- Design decision policies using tools like policy trees and machine learning models
- Build and solve optimization models using linear, integer, and nonlinear methods
- Combine machine learning outputs with optimization to improve decision-making
- Explore trade-offs using tools like what-if analysis and multi-objective thinking
- Apply prescriptive AI to real-world problems like logistics, planning, and resource allocation
Modules
This course has 3 modules
Meet your instructors
Dimitris Bertsimas
Vice Provost for Open Learning, MIT Open Learning
Dimitris Bertsimas is the Vice Provost for Open Learning at MIT, the Associate Dean of Business Analytics, the Boeing Leaders for Global Operations Professor of Management, and a Professor of Operations Research at MIT Sloan School of Management. At MIT Open Learning, he oversees Open Learning’s product offerings, new initiatives, infrastructure, finances, and operations.
