February 2
@
8:00 am
–
February 27
@
5:00 pm
Interpretability in AI
In this course, 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.
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 machine-learning challenge with interpretable methods.
Course At a Glance
Instructional Period: February 2 – February 27, 2026
Time to Complete Badge: 60 days
Last Day to Earn Badge: March 31, 2026
Expertise Level: Beginner/Intermediate
End-to-End Computer Vision
End-to-End Natural Language Processing
- Basic Python programming
- Basic understanding of machine learning models
- Basic understanding of deep learning models
- Basic PyTorch programming
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.
Upon completing this course, students will be able to do the following:
- Formulate a machine learning problem with interpretable models based on the specific task
- Develop basic interpretable machine learning models
- Develop model-agnostic methods for the interpretability in black-box machine learning models
- Develop example-based explanations for the interpretability in black-box machine learning models
- 2 Quizzes to learn basic knowledge of Interpretability in AI
- 1 coding assignment for students to develop basic interpretable machine learning models (e.g., linear regression, logistic regression, decision tree, etc.) to solve tasks by using deep learning packages such as PyTorch
- 1 coding assignment for students to develop model-agnostic methods, such as LIME and Shapley Values, for interpreting the black-box machine learning models
Module 1: Introduction to Interpretability in Machine Learning
Module 2: Develop Basic Interpretable Machine Learning Models
Module 3: Develop Model-Agnostic Methods
Module 4: Introduction to Example-based Explanations
The course starts on February 2, 2026. All coursework must be completed by March 31, 2026, in order to earn the micro-credential badge. You will continue to have access to the course materials until January 1, 2027. The approximate time to complete this course is 16 hours.
This course has an instructional period from February 2 to February 27, 2026. 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. 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 are provided within the course. No additional purchase is required.
Registration
Register for the course as a 1-credit independent study course with a maximum of 3 such TrAC courses per semester.
Students must register for the independent study by emailing benearl@iastate.edu and cc-ing baditya@iastate.edu. Also, fill this Google form for our records: https://forms.gle/d7PUHaqjko6sPHVg7
Note: You cannot cancel your registration after April 1 for any course.
INDUSTRY PROFESSIONALS/ISU STAFF/POSTDOCS
ISU Professionals/Staff and Government Employees: $300
About the Instructor
Zhanhong Jiang is a data scientist in the Translational AI Center (TrAC) at Iowa State University. His research interests lie in machine learning and distributed optimization. He has rich experience in developing AI/ML models/algorithms from theory to practice.
Contact: Nicole Hayungs
nhayungs@iastate.edu
