Document Type

Presentation

Publication Date

10-2026

Keywords

Agentic AI, AI Agents, Artificial Intelligence, Machine Learning, Bias-Variance Tradeoff, AI Hallucinations

Disciplines

Education | Engineering | Physical Sciences and Mathematics

Abstract

This workshop, part of the AI Demo Day Workshop Series hosted by the Paul English Applied AI Institute (PEAAII) at UMass Boston, explores the bias-variance tradeoff and how it affects the performance and reliability of AI models and agents. Led by Inal Mashukov, PhD Researcher at UMass Boston, the session covers concepts such as underfitting, overfitting, and regularization techniques to improve model performance. Through practical examples and hands-on exercises using PyTorch, participants explore why AI agents may hallucinate, repeat mistakes, or struggle to adapt, and learn approaches to building more reliable AI systems.

Comments

Free and open access to this work is made available to the UMass Boston community by ScholarWorks at UMass Boston. Those not on campus and those without a UMass Boston campus username and password may gain access to this work through Interlibrary Loan. If you have a UMass Boston campus username and password and would like to download this work from off-campus, click on the “Off-Campus Users” button.

Community Engaged/Serving

Part of the UMass Boston Community-Engaged Teaching, Research, and Service Series. //scholarworks.umb.edu/engage

Rights

© 2026 Inal Mashukov

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