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SUMMARY:Generative Models
DESCRIPTION:Generative Models\n\n\n\nThis 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. \n\n\n\n\n\nYou 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. \n\n\n\n\n\n\n\nCourse at a Glance\n\n\n\nCourse Hours: 16 hours \n\n\n\nInstructional Period: April 1 – April 30\, 2026 \n\n\n\nTime to Complete Badge: 60 days \n\n\n\nLast Dy to Earn Badge: May 15\, 2026 \n\n\n\nExpertise Level: Beginner/Intermediate \n\n\n\n\n\nThis course is part of the Foundational AI track in the TrAC Micro-Credential pathway at Iowa State University. \n\n\n\n\n\nFoundational AI courses\n\n\n\n\n\n\n\n\n\nMastering PyTorch\n\n\n\n\n\n\n\n\n\nEnd-to-End Computer Vision\n\n\n\n\n\n\n\n\n\nEnd-to-End Natural Language Processing\n\n\n\n\n\n\n\n\n\nGenerative Models\n\n\n\n\n\n\n\n\n\nMLOps\n\n\n\n\n\n\n\n\n\nInterpretability in AI\n\n\n\n\n\n\n\nLearn more about Micro-Credentials at Iowa State University! \n\n\n\n\n\n\n\n\n\n\nPrerequisites & intended Audience\n\n\n\n\n\n\n\n\n\n\nPrerequisites:Basic Python programmingBasic understanding of deep learning modelsBasic understanding of generative AIBasic PyTorch programming \n\n\n\nIntended 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.Use this space for describing your block. Any text will do. Description for this block. You can use this space for describing your block. \n\n\n\n\n\n\n\n\nLearning Outcomes\n\n\n\n\n\n\n\n\n\n\nBy the end of the course\, you should be able to: \n\n\n\n\nFormulate a generative AI problem and select the most appropriate generative model to solve a specific task\n\n\n\nDevelop basic variational Autoencoder model to complete the input data reconstruction and new data generation tasks\n\n\n\nDevelop basic generative adversarial network to address specific tasks in a given problem statement\n\n\n\nDesign advanced generative models with diffusion models to generate new perceptual data such as images and sound\n\n\n\n\n\n\n\n\n\nAssessments\n\n\n\n\n\n\n\n\n\n\n\n2 quizzes to understand the basic and fundamental knowledge of generative models\n\n\n\n1 coding exercise for students to develop basic variational Autoencoder to solve synthetic data generation tasks by using deep learning packages1 coding exercise for students to develop a custom generative adversarial network model to address a specific task or problem statement\n\n\n\n\n\n\n\n\n\nCourse Outline\n\n\n\n\n\n\n\n\n\n\n\nModule 1: Introduction to generative models and their applications\n\n\n\nModule 2: Design and develop variational Autoencoder\n\n\n\nModule 3: Develop generative adversarial networks\n\n\n\nModule 4: Develop diffusion models & deploy large language models\n\n\n\n\n\n\n\n\n\nCourse Procedures\n\n\n\n\n\n\n\n\n\n\n\nThe 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.\n\n\n\nThis 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.\n\n\n\nYou will receive the Generative Models micro-credential badge upon successful completion of the course assessments.\n\n\n\nCourse Materials:\n\n\n\nCourse materials are provided within the course. No additional purchase is required.\n\n\n\n\n\n\n\n\n\n\nRegistration\n\n\n\n\n\n\n\nStudents \n\n\n\nRegister for the course as a 1-credit independent study course with a maximum of 3 such TrAC courses per semester. \n\n\n\nStudents 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 \n\n\n\n\n\nIndustry Professionals/ISU Staff/Post Docs \n\n\n\n\n\n\n\n\n$\n\n\n\n500\n\n\n\n.00\n\n\n\n\n\nRegister Now\n\n\n\n\nISU Professionals/Staff and Government Employees: $300 \n\n\n\n\n\n\n\n\n\n\n\nAbout the Instructor\n\n\n\nZhanhong Jiang\, Research Scientist\n\n\n\nZhanhong 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.
URL:https://trac-ai.iastate.edu/event/generative-models/
CATEGORIES:Courses
ORGANIZER;CN="Nicole Hayungs":MAILTO:nhayungs@iastate.edu
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