Goal:
The goal of this course is to provide students with hands-on, practical mastery of generative artificial intelligence systems, with a strong focus on image generation and text generation using modern deep learning models. By the end of the course, students will be able to design, implement, fine-tune, and deploy generative AI solutions, critically evaluate their outputs, and apply them responsibly in real-world scenarios.
Course description:
This course offers a practice-focused introduction to Generative Artificial Intelligence, centered on two core application domains: AI image generation and AI text generation. Rather than emphasizing mathematical derivations, the course prioritizes implementation, experimentation, and project-based learning.
Students will work hands-on with state-of-the-art generative models such as diffusion models and large language models (LLMs), using modern frameworks and tools. Through two major semester projects, students will build complete generative AI pipelines, from data preparation and prompt engineering to fine-tuning, evaluation, and deployment considerations.
Ethical, legal, and societal implications of generative AI are integrated throughout the course, with a focus on responsible use, bias, copyright, and reproducibility.