AI Magic Lab
A rigorous course structure integrating four major sections: AI Fundamentals, Large Model Generation (GenAI & LLM), Agents and Evolutionary Computation (highlighted as a PolyU Feature), and Ethics. The course logic progresses sequentially through Perception & Data (L1-3), Cognition & Generation (L4-6), Agents & Evolution (L7-9), and concludes with Ethics & Future (L10).
Course Overview
Content Summary
The "AI Magic Lab" is a rigorous, integrated course designed to provide a deep, sequential understanding of modern Artificial Intelligence. The curriculum is structured into four progressive modules: foundational concepts (Perception & Data), advanced generative capabilities (Cognition & Generation via LLMs and Diffusion Models), autonomous systems (Agents & Evolutionary Computation), and ethical governance (Ethics & Future). Students will move from understanding the raw numerical representation of data to mastering complex system design, culminating in a comprehensive view of responsible AI creation and deployment.
This course provides a rigorous, integrated understanding of modern AI, covering core data fundamentals, Large Language Model (LLM) generation techniques, the architecture of autonomous Agents, and the critical ethical considerations necessary for responsible deployment.
Learning Objectives
- Master the fundamentals of AI perception, data representation (Tensors), and foundational supervised learning tasks like Classification.
- Understand and control Large Language Models (LLMs) and Generative AI by applying concepts of sequence prediction, the Attention Mechanism, and advanced Prompt Engineering techniques.
- Design and analyze Intelligent Agents, integrating the Perception-Decision-Action loop with advanced, population-based optimization methods such as Evolutionary Computation.
- Differentiate between Generative and Discriminative AI and explain the mechanical process of Diffusion Models for Text-to-Image generation.
- Evaluate the ethical challenges inherent in contemporary AI (data bias, model hallucination, deepfakes) and propose strategies for responsible Human-AI Symbiosis.