Study-unit Aims

This course aims to provide undergraduate students with a comprehensive understanding of various generative AI techniques and their applications in diverse fields such as computer vision, natural language processing, and creative arts. Through theoretical instruction and hands-on projects, students will develop the necessary skills to design and implement generative models, fostering their ability to address real-world challenges using state-of-the-art AI techniques.

Learning Outcomes: Knowledge and Understanding

By the end of the study-unit the student will be able to:

1. Demonstrate that they understand the basics of generative modeling and its significance in AI applications;
2. Employ advanced generative models clearly showing they apprehend their delicate nature and their suitability for different AI tasks;
3. Recognize and tackle the ethical challenges posed by generative AI and propose strategies for responsible AI development and deployment.

Learning Outcomes: Skills

By the end of the study-unit the student will be able to:

1. Implement GANs for image synthesis and recognize potential challenges in GAN training;
2. Construct VAEs for data reconstruction and latent space exploration;
3. Create Neural Net-based models for text generation and analyze their performance;
4. Implement generative models in computer vision tasks and assess their effectiveness in various scenarios;
5. Design and implement a sophisticated generative AI models to solve real-world problem.