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Generative Models

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Master Generative Models: Your Path to AI Excellence

Course Description

Elevate your machine learning skills with our comprehensive course, “Generative Models”. This course covers everything you need to know about generative models, from the basics of discriminative vs. generative models to advanced techniques like variational autoencoders, generative adversarial networks, and diffusion models.

Engage in hands-on activities, solve real-world problems in image and time-series generation, and receive 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 generative models. Join us and become a leader in the AI field!

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

Foundations of AI Pathway Courses:

Prerequisite

  • Basic Python programming
  • Basic understanding of deep learning models
  • Basic understanding of generative AI
  • 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 generative AI problem and select the most proper generative models to solve a specific task.
  • Design and develop basic variational Autoencoder model to address specific tasks.
  • Design and develop basic generative adversarial network model to address specific tasks.
  • Design advanced generative models with diffusion models.
  • 1 Quiz to learn basic knowledge of discriminative and generative models.
  • 3 Coding exercise questions which would include implementing Python codes based on hands-on activities. This would include coding a variational Autoencoder, a generative adversarial network and a diffusion model, and hyperparameter tuning for model optimization.
  • Module 1: Introduction to generative models and their applications
  • Module 2: Design and develop variational Autoencoder
  • Module 3: Develop generative adversarial networks
  • Module 4: Develop diffusion models

Course Procedures

The course starts on November 4, 2024. All coursework must be completed before the course ends on December 15, 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)
*$300.00 Student & Government Employee

Course Hours: 16 hours

Course Start Date: November 4, 2024

Last Day to Register: November 8, 2024

Course End Date: December 15, 2024

Course Access Time: 41 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.