Core Concepts of Generative AI is an introductory–to–intermediate course designed to equip learners with a strong foundational understanding of generative artificial intelligence—its theories, methods, tools, and real-world applications. This course demystifies how modern AI systems create text, images, audio, and other content, while helping students develop the technical intuition needed to work confidently with generative models.
Learners will explore the evolution of generative AI, from early probabilistic models to today’s large language models (LLMs) such as GPT, Claude, Llama, and diffusion-based image generators like Stable Diffusion and Midjourney. Through hands-on exercises, students will practice prompt engineering, fine-tuning, evaluation methods, and responsible AI principles.
By the end of the course, students will understand how generative AI works, how to use it effectively, and how to apply it to real-world tasks across industries such as education, marketing, software development, and creative content production.
Learning Outcomes
Upon completing this course, learners will be able to:
- Explain the fundamental concepts behind generative AI and machine learning.
- Understand the architecture and training principles of large language models and diffusion models.
- Understand generative AI tools.
- Evaluate generative AI outputs for accuracy, bias, and safety.
- Understand model fine-tuning, and embeddings.
- Apply generative AI to solve practical problems through mini-projects.
Topics Covered:
- Introduction to Artificial Intelligence & Machine Learning
- Large Language Models (GPT, Llama, Claude, Gemini)
- Transformers & Attention Mechanisms
- Diffusion Models for Image Generation
- Fine-tuning and Masked Language Models Concepts
- Introduction to BLEU
- Introduction to FID
- Retrieval-Augmented Generation (RAG)
- Real-world Applications Across Industries
