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Introduction to Deep Learning
Deep learning is a sub-field of machine learning that focuses on learning complex, hierarchical feature representations from raw data using artificial neural networks. The course covers fundamental principles, underlying mathematics, optimization concepts (gradient descent, backpropagation), network modules (linear, convolution, pooling layers), and common architectures (CNNs, RNNs). Applications demonstrated include computer vision, natural language processing, and reinforcement learning. Students will use the PyTorch deep learning library for implementation and complete a final project on a real-world scenario.
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).
AI Mastery Bootcamp: From Zero to Agent Architect
A 5-session intensive bootcamp designed to transform beginners into AI Agent Architects. The curriculum covers the 'BRIC' framework for prompt engineering, content acceleration for reading and viral copywriting, workplace automation for Excel and PowerPoint, building a 'Second Brain' using RAG (Retrieval-Augmented Generation), and creating autonomous digital employees.
Prompt Engineering Advanced Guide
A comprehensive advanced guide to mastering AI through structured logic and precise instruction. The course covers structural frameworks (CO-STAR), Few-Shot learning, Chain of Thought reasoning, output format constraints (JSON/Markdown), and prompt system management to resolve issues such as AI hallucinations and poor logical output.
OpenClaw: Architecture, Dev & Security for Local AI Agents
This course provides an in-depth analysis of OpenClaw, a groundbreaking open-source framework for autonomous AI agents. It systematically deconstructs the framework's layered system architecture, local-first RAG memory mechanisms, browser automation protocols, and highly scalable skill ecosystem. The curriculum covers practical orchestration of complex workflows, including PIV automation flows and multi-agent committee patterns. Furthermore, it critically analyzes hardware trade-offs in production-grade deployment paradigms and presents defense-in-depth strategies against core security threats such as RCE vulnerabilities and prompt injection. The course aims to empower senior developers and architects to build AI agent systems that possess high autonomy while remaining secure and controllable.
LLMs for Everyone: From Basics to Practical Use (2026 Edition)
This course is a beginner-friendly, practical introduction to Large Language Models (LLMs) such as ChatGPT and Gemini. Designed for learners from any background, it explains how LLMs work at a high level, what they can and cannot do, and how to use them effectively in study, work, and everyday life. Through hands-on demonstrations and guided exercises, you will learn prompt techniques, how to evaluate outputs critically, how to handle hallucinations and bias, and how to use common tools (e.g., documents, summaries, translation, data tasks) safely and responsibly. By the end of the course, you will be able to build a personal “LLM workflow” for real tasks—writing, research, planning, and productivity—without needing advanced coding skills.
Lighthouse for Hong Kong Integrated Practice: Book 9
A comprehensive English language practice workbook designed for Hong Kong students, focusing on reading comprehension, thematic vocabulary expansion, and practical grammar applications across five core units.
【人教版】初中英语 七年级下册
本书为人民教育出版社出版的七年级下册英语教材，依据2022年版课程标准编写。全书包含八个单元，涵盖动物、规则、健康、饮食、生活、天气、经历和故事等主题，通过听说读写及语音、语法、词汇的综合训练，全面培养学生的英语核心素养。
Maths in Action (Primary 1-3)
This Primary 1 to Primary 3 Mathematics Curriculum is designed to build a solid and comprehensive mathematical foundation for early learners. The syllabus is systematically structured across five core strands: Number, Measures, Shape and Space, Data Handling, and Further Learning. Throughout this stage, students will progress from basic number recognition and arithmetic operations to developing spatial awareness, mastering practical measurement skills, and learning introductory data visualization. Beyond theoretical knowledge, the curriculum emphasizes cultivating logical thinking and problem-solving abilities, encouraging students to apply abstract mathematical concepts to real-world scenarios.
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