Academics / Courses / DescriptionsIEMS 490: Adv Topics in Large Foundation Models
VIEW ALL COURSE TIMES AND SESSIONS
Prerequisites
Solid understanding of machine learning fundamentals Proficiency in programming (e.g., Python) Familiarity with deep learning frameworks (e.g., TensorFlow, PyTorch) Basic knowledge of natural language processing and computer vision conceptsDescription
This course provides an in-depth exploration of advanced topics in the field of large foundation models, focusing on the cutting-edge techniques, applications, and challenges. Students will gain a comprehensive understanding of the state-of-the-art models, including their model architecture, training methodologies, and practical implementations. The course will cover a range of topics, including but not limited to natural language processing, computer vision, reinforcement learning, and multimodal learning. Emphasis will be placed on hands-on projects and research-oriented assignments to foster skills and encourage exploration in this evolving field.
Syllabus:
Week 1: Introduction to Large Foundation Models
Week 2: Model Architectures and Variants
Week 3: Training Methodologies
Week 4: Ethical Considerations
Week 5: Multimodal Learning
Week 6: Generative Models
Week 7: Reinforcement Learning and Large Models
Week 8: Advanced Applications
Week 9-10: Project Work and Research Exploration
Weekly assignments (30%)
Midterm exam (20%)
Final project and presentation (40%)
Class participation and engagement (10%)