| CODE | ARI3333 | ||||||||||||
| TITLE | Generative AI | ||||||||||||
| UM LEVEL | 03 - Years 2, 3, 4 in Modular Undergraduate Course | ||||||||||||
| MQF LEVEL | 6 | ||||||||||||
| ECTS CREDITS | 5 | ||||||||||||
| DEPARTMENT | Artificial Intelligence | ||||||||||||
| DESCRIPTION | The study-unit introduces the fundamental concepts and techniques of generative artificial intelligence (AI). Throughout the course, students will delve into various key points, including Generative Adversarial Networks (GANs) models, Variational AutoEncoders (VAEs) models, Autoregressive models, Transformer models, and the training and evaluation of generative models. Furthermore, students will apply generative models to diverse fields such as image generation, music composition, and natural language processing. The curriculum also incorporates discussions on Ethical Considerations and Social Implications, guiding students to understand the broader impact of AI. Moreover, the course explores future directions, including Self-Supervised Learning and other emerging trends. By the end of this course, students will have a comprehensive understanding of the theory and application of generative AI, preparing them to address the ethical, social, and technical challenges in this rapidly evolving field. 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: 1. Knowledge & Understanding By the end of the study-unit the student will be able to: a) Demonstrate that they understand the basics of generative modeling and its significance in AI applications; b) Employ advanced generative models clearly showing they apprehend their delicate nature and their suitability for different AI tasks; c) Recognize and tackle the ethical challenges posed by generative AI and propose strategies for responsible AI development and deployment. 2. Skills By the end of the study-unit the student will be able to: a) Implement GANs for image synthesis and recognize potential challenges in GAN training; b) Construct VAEs for data reconstruction and latent space exploration; c) Create Neural Net-based models for text generation and analyze their performance; d) Implement generative models in computer vision tasks and assess their effectiveness in various scenarios; e) Design and implement a sophisticated generative AI models to solve real-world problem. Main Text/s and any supplementary readings: Main Text - David M. Patel, (2023) Artificial Intelligence & Generative AI for Beginners: The Complete Guide Paperback. Supplementary Text - Alger Fraley (2023) The Artificial Intelligence and Generative AI Bible: [5 in 1] The Most Updated and Complete Guide | From Understanding the Basics to Delving into GANs, NLP, Prompts, Deep Learning, and Ethics of AI. |
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| STUDY-UNIT TYPE | Indep Study, Lect, Tutorial & Online Learning | ||||||||||||
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| LECTURER/S | Vanessa Camilleri Alexiei Dingli Ingrid Galea Kristian Guillaumier Konstantinos Makantasis Matthew Montebello (Co-ord.) Dylan Seychell |
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The University makes every effort to ensure that the published Courses Plans, Programmes of Study and Study-Unit information are complete and up-to-date at the time of publication. The University reserves the right to make changes in case errors are detected after publication.
The availability of optional units may be subject to timetabling constraints. Units not attracting a sufficient number of registrations may be withdrawn without notice. It should be noted that all the information in the description above applies to study-units available during the academic year 2024/5. It may be subject to change in subsequent years. |
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