This course provides an in-depth exploration of cutting-edge AI technologies and techniques, with a particular focus on generative models. Students will study foundational and emerging AI methods, including deep learning, reinforcement learning, generative adversarial networks (GANs), transformers, and large language models (LLMs). Through theoretical and hands-on approaches, the course will examine how these models are trained, optimized, and applied to various domains such as natural language processing, image generation, and autonomous systems. By the end of the course, students will be prepared to implement and critically analyze AI systems that generate content autonomously. Prerequisites: Admission to either the graduate program in Electrical, Computer or Software Engineering or Engineering program advisor's permission
This course provides an in-depth exploration of cutting-edge AI technologies and techniques, with a particular focus on generative models. Students will study foundational and emerging AI methods, including deep learning, reinforcement learning, generative adversarial networks (GANs), transformers, and large language models (LLMs). Through theoretical and hands-on approaches, the course will examine how these models are trained, optimized, and applied to various domains such as natural language processing, image generation, and autonomous systems. By the end of the course, students will be prepared to implement and critically analyze AI systems that generate content autonomously. Prerequisites: Admission to either the graduate program in Electrical, Computer or Software Engineering or Engineering program advisor's permission
