You will engage in hands-on activities, homework, and instructor consulting to make learning generative models 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 generative models.
Generative Models
April 1 @ 8:00 am – April 30 @ 5:00 pm
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, diffusion models, and large language models. By the end of this course, you’ll have the knowledge and confidence to tackle machine-learning challenge using generative models.
Course at a Glance
Mastering PyTorch
End-to-End Computer Vision
End-to-End Natural Language Processing
Generative Models
MLOps
Interpretability in AI
Prerequisites:
Basic Python programming
Basic understanding of deep learning models
Basic understanding of generative AI
Basic PyTorch programming
By the end of the course, you should be able to:
- Formulate a generative AI problem and select the most appropriate generative model to solve a specific task
- Develop basic variational Autoencoder model to complete the input data reconstruction and new data generation tasks
- Develop basic generative adversarial network to address specific tasks in a given problem statement
- Design advanced generative models with diffusion models to generate new perceptual data such as images and sound
- 2 quizzes to understand the basic and fundamental knowledge of generative models
- 1 coding exercise for students to develop basic variational Autoencoder to solve synthetic data generation tasks by using deep learning packages
1 coding exercise for students to develop a custom generative adversarial network model to address a specific task or problem statement
- 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 & deploy large language models
- The course starts on April 1, 2026. All coursework must be completed by May 15, 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 April 1 to April 30, 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 and answering any questions you have.
- You will receive the Generative Models micro-credential badge upon successful completion of the course assessments.
- Course Materials:
- Course materials are provided within the course. No additional purchase is required.
Students
Industry Professionals/ISU Staff/Post Docs
$
500
.00
Nicole Hayungs
nhayungs@iastate.edu