CSCA 5112: Introduction to Generative AI
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- Course Type: Elective
- Specialization: Generative AI
- Instructor:ÌýDr. Tom Yeh, Professor of Computer Science
- Prior knowledge needed:
- Programming languages: N/A
- Math: Basic to intermediate Linear Algebra, Trigonometry, Vectors & Matrices
- Technical requirements: N/A
Learning Outcomes
- Learn the key models for Generative AI, including ChatGPT and the Transformer for text, and the GAN and Ìýthe Diffusion Model for images.
- Develop a strong theoretical foundation and practical math skills for Generative AI.
- Understand the capabilities and limitations of Generative AI.
Course Grading Policy
Assignment | Percentage of Grade |
|---|---|
ChatGPT Graded Exam | 20% |
Generative Adversarial Network Graded Exam | 20% |
Transformer Graded Exam | 20% |
Diffusion Model Graded Exam | 20% |
CSCA 5112 Introduction to Generative AI Final Exam | 20% |
Course Content
Duration: 3Ìýhours
Welcome to "Introduction to Generative AI." This first week, you will learn about ChatGPT, the first generative AI system that gained world-wide attention, ushering in a new era of AI research!
Duration: 3Ìýhours
This week, you will learn about the Generative Adversarial Network, the first successful deep learning approach to generating realistic looking images, which started a new wave of generative AI research.
Duration: 3Ìýhours
This week, you will learn about the Transformer Model, which is the model behind most of the state-of-the-art systems for generative text, including ChatGPT.
Duration: 3Ìýhours
This week, you will learn about Diffusion Model, which is the model behind most of the state-of-the-art systems for generative images.
Duration: 2Ìýhours
This module contains materials for the final exam. If you've upgraded to the for-credit version of this course, please make sure you review the additional for-credit materials in the Introductory module and anywhere else they may be found.
The final exam is a graded assignment of single answer and multiple choice questions.
Notes
- Cross-listed Courses: CoursesÌýthat are offered under two or more programs. Considered equivalent when evaluating progress toward degree requirements. You may not earn credit for more than one version of a cross-listed course.
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