Introduction to Generative AI Course Syllabus
Full curriculum breakdown — modules, lessons, estimated time, and outcomes.
Overview (80-120 words) describing structure and time commitment.
Module 1: Introduction to Generative AI
Estimated time: 4 hours
- Understand what generative AI is and how it differs from traditional AI
- Explore the evolution of AI from rule-based systems to deep learning models
- Learn how generative AI models create new content
- Identify key applications across industries
Module 2: Types of Generative AI Models
Estimated time: 6 hours
- Study the architecture and function of GANs (Generative Adversarial Networks)
- Understand how transformers power language models like GPT
- Learn how diffusion models generate images using noise reduction
- Explore real-world models like DALL·E and Stable Diffusion
Module 3: Applications of Generative AI
Estimated time: 8 hours
- Discover how generative AI is used in marketing and content creation
- Examine applications in healthcare, gaming, and finance
- Learn about AI-generated art and automated storytelling
- Understand the role of deepfakes and synthetic media
Module 4: Large Language Models and Reinforcement Learning
Estimated time: 5 hours
- Understand the role of large language models like ChatGPT and Bard
- Explore how LLMs process and generate human-like text
- Learn how reinforcement learning improves model outputs
Module 5: Ethical Considerations and AI Bias
Estimated time: 7 hours
- Identify risks of AI-generated misinformation and bias
- Learn about responsible AI development practices
- Discuss privacy concerns and regulatory frameworks
Module 6: Final Project
Estimated time: 10 hours
- Apply generative AI tools to create text, images, or video content
- Develop a small AI-powered application or analyze existing AI-generated content
- Present findings on AI’s impact on future innovation
Prerequisites
- Basic understanding of computers and the internet
- No prior AI or programming experience required
- Interest in artificial intelligence and emerging technologies
What You'll Be Able to Do After
- Explain how generative AI differs from traditional AI
- Describe key generative AI models including GANs, transformers, and diffusion models
- Identify real-world applications of generative AI across industries
- Recognize ethical concerns such as bias, misinformation, and privacy risks
- Apply generative AI tools to create or analyze content in a practical project