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AI Overfitting, Hallucination, Spurious Correlations Fundamentals
"AI Overfitting, Hallucination, Spurious Correlations Fundamentals" focuses on common AI modeling issues and their consequences. Participants learn to identify and correct these problems through better data handling and model design. Cybersecurity concerns include adversarial inputs that exploit model weaknesses to produce false outputs. The course provides tools to defend against such manipulation, ensuring AI outputs remain reliable and secure.
Course Overview
None
Learning Objectives
- Understand the fundamentals of AI overfitting, hallucination, and spurious correlations.
- Analyze the causes and consequences of these AI limitations.
- Evaluate the strategies for detecting and mitigating these issues.
- Identify the key considerations for building robust and reliable AI models.
- Discuss the impact of these limitations on AI performance and trust.
Framework Connections
The materials within this course focus on the NICE Framework Task, Knowledge, and Skill statements identified within the indicated NICE Framework component(s):
Competency Areas
Feedback
If you would like to provide feedback on this course, please e-mail the NICCS team at NICCS@mail.cisa.dhs.gov. Please keep in mind that NICCS does not own this course or accept payment for course entry. If you have questions related to the details of this course, such as cost, prerequisites, how to register, etc., please contact the course training provider directly. You can find course training provider contact information by following the link that says “Visit course page for more information...” on this page.