Interpretability in AI

Course Description

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.

Complete any 3 courses listed below to earn the Foundations in AI badge:

• Machine Learning Operations (MLOps)
• End-to-End Computer Vision
• Generative Models
• Mastering PyTorch
• End-to-End Natural Language Processing
• Interpretability in AI

Learn more about Micro-Credentials at Iowa State University!

Prerequisites

• 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.

Course Procedures

The course starts on March 3, 2025. All coursework must be completed by April 30, 2025, in order to earn the micro-credential badge. You will continue to have access to the course materials until January 1, 2026. The approximate time to complete this course is 16 hours. 

This course has an instructional period from March 3 to March 30, 2025. During this instructional period, course materials will be released weekly and live synchronous sessions will be held. You may complete the course materials at your own pace. Live Zoom meetings will be conducted for interactive coding sessions. A suitable time for these live sessions will be determined through a group poll. The recordings of those sessions will be available soon after each meeting.

You will receive the Interpretability in AI micro-credential badge upon successful completion of the course assessments.

Course Materials

Course materials are provided within the course. No additional purchase is required.

Contact Information

Contact isopd@iastate.edu for more information.

Course Developer

The Translational AI Center breaks down disciplinary silos to bring together core Iowa State artificial intelligence researchers and subject matter experts interested in applying new technologies to their work. For more information, visit Translational AI Center at Iowa State University

Instructor

Zhanhong Jiang, Data Scientist

Zhanhong Jiang is a data scientist in the Translational AI Center (TrAC) at Iowa State University. His research interests lie in decentralized deep learning, reinforcement learning, time-series prediction and applications to cyber-physical systems. Prior to that, he was a senior AI scientist at Johnson Controls and worked on smart and healthy building solutions using AI/ML technologies. He has numerous publications in prestigious journals and conferences and more than 10 patents.