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Graph Neural Network

Registration Now Open!

Master Graph Neural Networks: From Basics to Real-World Applications

Course Description

Elevate your machine learning skills with our comprehensive course, “Graph Neural Network”. 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!

This course is one of 6 courses in the Advanced AI Techniques pilot micro-credential pathway offered by the Translational AI Center at Iowa State University.

Advanced AI Techniques Pathway Courses:

Prerequisite

  • Basic Python programming
  • Basic understanding of deep learning
  • Basic understanding of graphical concepts
  • 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 AI and how to use PyTorch for a broad range of audiences.

  • Learning Outcomes
  • Assessments
  • Course Outline

By the end of the course, you should be able to:

  • 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.
  • Develop and implement advanced graph neural network models.
  • 1 Quiz to learn basic knowledge of nodes, edges, and graphs,
  • 3 Coding exercise questions which would include implementing Python codes based on hands-on activities. This would include 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

Course Procedures

The course starts on October 7, 2024. All coursework must be completed before the course ends on November 3, 2024. The approximate time to complete this course is 16 hours. You can complete the modules 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 a micro-credential badge upon completing the assessments at the end.

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

Translational_AI Center

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

 

Registration Cost: $750.00 $500.00 USD (Initial Promo)
*$300 Student & Government Employee

Course Hours: 16 hours

Course Start Date: October 7, 2024

Last Day to Register: October 11, 2024

Course End Date: November 3, 2024

Course Access Time: 27 Days

*At the time of registration, you’ll be asked to create an account for this course. Use an email address ending in “.edu” or “.gov” to receive a discount. $200.00 will be immediately credit back after purchase.

Instructor

 

Zhanhong Jiang stands in front of a blackboard filled with mathematical equations, explaining the content.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.