September 1
@
8:00 am
–
October 31
@
5:00 pm
Graph Neural Networks
Elevate your machine learning skills with our comprehensive course, “Graph Neural Networks”. This course covers everything you need to know about graph neural network models, including the basics of graph machine learning, advanced graph neural networks with various mechanisms, and how to leverage these models to address specific real-world problems.
In this course, you will engage in hands-on activities and solve real-world problems such as in image recognition and time-series prediction, while receiving expert guidance from our instructors. By the end of this course, you’ll have the knowledge and confidence to tackle any machine-learning challenge using graph neural networks. Join us and become a leader in the AI field!
Course At a Glance
Instructional Period: September 1 – September 28, 2025
Time to Complete Badge: 60 days
Last Dy to Earn Badge: October 31, 2025
Expertise Level: Intermediate/Advanced
Scientific Machine Learning
Parallelism in Deep Learning
- Basic Python programming
- Basic understanding of deep learning
- Basic understanding of graphical concepts
- 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 advanced understanding of graph neural networks for professionals ready to tackle complex AI challenges.
By the end of the course, you should be able to:
Develop and implement advanced graph neural network models
Formulate a learning problem based on a task by using graph neural network model
Design and develop basic graph neural network architectures to address specific tasks
Propose, develop, and implement graph neural network models with convolutional and recurrent mechanisms to address tasks
- 1 Quiz to learn basic knowledge of nodes, edges, and graphs
- 3 Coding Exercise Questions which include implementing Python codes based on hands-on activities. This includes coding a basic graph neural network architecture, graph convolutional network, and hyperparameter tuning for model optimization.
Module 1: Introduction to graph-structured data and graph learning
Module 2: Design basic graph neural networks
Module 3: Develop advanced graph neural network with convolutional and recurrent mechanisms
Module 4: Advanced graph neural networks
The course starts on September 1, 2025. All coursework must be completed by October 31, 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 September 1 to September 28, 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 Graph Neural Networks 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/mQRefUJ4qpa29s5w6
Industry Professionals/ISU Staff/Post Docs
$
500
.00
ISU Professionals/Staff and Government Employees: $300
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.
