Lecturer: Nora Grigoryan, Emerson
Date: Friday, September 5
Time: 5 PM
Venue: Museum Lecture Hall

Description:

Machine learning models can be powerful, but their success depends on the quality and relevance of the data. Sometimes, even with data available, models fail because the data doesn’t support the business question.

 

This lecture explores how data limitations can undermine model reliability, why accuracy alone isn’t enough, and how to ensure your models are truly aligned with business needs. The goal is to build not just predictive models, but trustworthy ones that drive real decisions.

 

Seminar Content:

 

• Overview

– Difference between ML, AI and Gen AI

 

• Project-Specific Challenges in Forecasting and Classification