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
Data-Driven Prescriptive AI
Model-Driven Prescriptive AI, Part 1
Model-Driven Prescriptive AI, Part 2
How you'll learn
Real-World LearningLearn from MIT faculty and experts who ground their teaching in real-world cases rather than mathematical models, making the material approachable for all.
Practical ApplicationApply your new knowledge with hands-on, practical exercises drawn from healthcare, sports, finance, sustainability, and more.
AI-Enabled SupportDeepen your understanding of the course material and get help on assignments from AskTIM, the AI assistant built by MIT researchers.
Stackable CredentialsEarn an MIT Open Learning certificate at each milestone—module, course, and program—demonstrating your AI expertise. Available in paid courses only.
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.
FAQs
There are no prerequisites for the courses in this program, and no coding background is required.
Yes, you will receive a verified certificate of completion for this course. Additionally, if you complete the full Universal AI program, you will receive a Program certificate.
Throughout the course, you'll complete short multiple-choice knowledge checks after videos or sections to confirm your understanding, plus broader multiple-choice assignments at the end of each module that cover the full range of material. Some assignments use coding notebooks, giving you hands-on practice interpreting AI outputs. However, you won't need to write any code — all code is provided, so you can focus on reading, running, and interpreting the results. There are no exams.
This course helps you go beyond reporting on what data says in order to learn how to use it to make smarter decisions. You'll learn to:
- Tell the difference between predicting outcomes and deciding next steps. You'll learn how knowing what is likely to happen differs from knowing the best action to take.
- Turn data into action. You'll learn how to take what the data shows and turn it into a clear recommendation.
- Determine the best option for business problems within your limits. You will learn how to make strong decisions even when working with real-world constraints like budgets, time or limited resources.
