Graduates will learn to:

1. Situate Generative Artificial intelligence (AI) within the broader history, context, and applications of artificial intelligence and deep learning.
2. Explore and Adapt advanced techniques in AI model fine-tuning with Hugging Face and PyTorch tool to perform tasks in novel contexts.
3. Use LLMs, retrieval-augmented generation (RAG) and prompt engineering to create a custom chatbot.
4. Use image generation models such as Segment Anything Model (SAM) and Stable Diffusion to replace parts of images with AI-generated content.
5. Build applications that use LLMs for content generation, vector databases, semantic search and RAG techniques to transform data listings into narratives.

Assessment

Assessments are through compulsory project submissions

Level

Intermediate

Certification

Students will receive an e-certificate only when they passed all their projects.