Interpretability in AI

This educational resource is no longer available.

In this course, "Interpretability in AI", you will learn about various interpretable and explainable machine learning algorithms, a branch of machine learning and AI. This course covers everything you need to know about interpretability, including an overview of basic concepts of interpretability, interpretable models, model-agnostic methods, and example-based explanations. You will also learn how to leverage these interpretable approaches to address the specific real-world problems.

In this course, you will engage in hands-on activities, homework, and instructor consulting to make learning Interpretability in AI enjoyable and rewarding. You will also be able to tackle real-world problems in science and engineering. By the end of this course, you’ll have the skills and confidence to tackle any machine-learning challenge with interpretable methods.

This course is one of 6 courses in the Foundations in AI pilot Micro-Credential pathway offered by the Translational AI Center at Iowa State University.

For more information regarding the course including: Learning Outcomes, Assessments, and a Course Outline please visit the Interpretability in AI course page from Iowa State Online.

Prerequisite

Basic Python programming
Basic understanding of machine learning models
Basic understanding of deep learning models
Basic PyTorch programming

Intended Audience
The course is intended for a broad audience within the spectrum of the software and technology industry, including software engineers, data scientists, data engineers, data analysts, research scientists, and software developers. The course is designed to provide a basic understanding of Interpretability in AI and how to use these methods for a broad range of audiences.

Pages / Length:
Publication Date: 12/2024

Permanent link for this product: https://store.extension.iastate.edu/product/17180