The landscape of software products is being redefined by artificial intelligence. This course offers product managers, engineers, and technology leaders a deep introduction to machine learning, from foundational models that turn data into predictions to the architectures powering today’s Generative AI and AI Agents.

Unlike application-only AI courses, IML emphasizes understanding how machine learning works. Students build intuition for core principles (statistics, regression, classification, optimization, generalization), examine how models are trained and evaluated, and learn to interpret model performance and limitations.

Through hands-on coding labs in modern ML environments, students construct and validate classical ML models, connect them to modern generative and agentic systems, and practice responsible deployment of AI capabilities.

The course culminates in a project where students design and prototype an AI‑infused product feature, integrating both product thinking and end‑to‑end ML workflows.

Registration

• MSSM students – You don't need to obtain instructor approval. Register for the course in SIO and you'll be added to the roster.
• Non-MSSM students – Instructor approval is required. Register for the course in SIO and you will be placed in the waitlist. Click here to submit your request for approval to the instructor. Waitlist will be cleared one month before the course begins.

Downloads

All downloads require Andrew login. Files are subject to change and latest versions are available on course Canvas pages.

• Course Syllabus - Spring 2025
• Course Modules - Spring 2024 (Will be updated once the course begins)
• Sample Product Assignment 1 - Data Product Exploration
• Sample Product Assignment 3 - Capability Analysis
• Sample Coding Assignment 3 - Linear Regression
• Sample Coding Assignment 5 - Model Selection

FAQs

• What kinds of projects and hands‑on work will we do?
  You will complete several small, focused modeling assignments and one larger course project. The hands‑on work includes building and evaluating classical machine learning models (such as regression, classification, and tree/ensemble models) on real datasets in notebooks, performing error and bias analysis, and writing brief interpretations of your results. For the course project, you will design and document an AI‑infused product feature and support it with a working ML prototype that demonstrates how the feature could function in a real product.
• How does this course help with an AI Product Manager career?
  The course is designed for product managers and engineers who want to work effectively with machine learning. You will learn how core ML models actually work, how to frame product problems as ML tasks, how to interpret model performance and limitations, and how to communicate with data scientists about trade‑offs, risks, and requirements. The final AI‑infused product feature gives you a concrete project you can discuss in interviews and use as evidence of your ML understanding.
• Do I need prior machine learning experience to take this course?
  No prior machine learning or AI coursework is required. You do need basic Python programming skills and some comfort working with data, but the course builds ML concepts from first principles and then connects them to modern systems like Generative AI and AI Agents at a conceptual and applied level.
• How technical is the course?
  This is a technical course, but it emphasizes intuition and practical reasoning over heavy math. You will work with code, data, and models regularly, but the focus is on understanding how models learn, how to evaluate them, and how they enable product capabilities, rather than on deriving algorithms from scratch.
• Is this course overlapping with other AI or ML courses in the MSSM program?
  This course focuses on how machine learning works internally covering core principles, model behavior, and evaluation, while other MSSM AI courses focus more on applying or operationalizing AI systems. It is intended to give you a strong foundation you can build on in more specialized or advanced classes.