人工智慧總整與實作

Welcome to AI Capstone Spring 2026! In recent years, we have witnessed remarkable breakthroughs in artificial intelligence—from ChatGPT reshaping how humans interact with language to Google Veo 3.1 expanding the frontier of video generation. As the field evolves, attention is now shifting toward Agentic AI, where systems are no longer merely reactive, but capable of goal-driven, autonomous decision-making. Yet the next major frontier extends beyond the screen. We are entering the era of Physical AI, where intelligence moves from the digital world into the physical one. Physical AI refers to AI systems that can perceive, reason, and act within real-world environments—sensing through cameras, microphones, and other sensors, and interacting with the world via motors, robotic arms, wheels, or other actuators. In this AI Capstone course, Physical AI serves as the central lens through which all projects and discussions are contextualized. Students will explore how modern AI models are integrated into real-world systems such as robots, smart homes, and autonomous vehicles, and how intelligence, embodiment, and environment jointly shape system behavior. As AI systems increasingly operate around us in everyday life, critical questions emerge: How do these systems function in real-world settings? How do we ensure their reliability, safety, and alignment with human values? And what new challenges arise when AI is deployed in open, dynamic environments? If these questions inspire you, this course is designed for you. Intended Learning Outcomes: 1. Explain the definition, scope, and significance of Physical AI, and distinguish it from purely digital and agentic AI systems. 2. Describe the core concepts, system architectures, and key enabling technologies underlying Physical AI, including sensing, perception, reasoning, planning, and control. 3. Analyze and apply foundational Physical AI techniques to real-world problem settings, taking into account environmental dynamics, embodiment, and system constraints. 4. Identify and evaluate major application domains and representative use cases of Physical AI, such as robotics, smart environments, and autonomous systems. 5. Design, implement, and prototype a Physical AI solution that demonstrates the integrated use of perception, decision-making, and physical action. Note: The lectures during the week of the 12th–14th will be co-taught with Chun-yien Chang, a Ph.D. candidate in the Department of Computer Science at National Yang Ming Chiao Tung University. Chun-yien will cover ontology engineering and ontology-based evaluation.

Introduction to Artificial Intelligence, Introduction to Machine Learning, Linear Algebra, Probability, and Python/C++

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Computer Assignment: 50% - HW1: Coordinate Transformation - HW2: 3D Scene Reconstruction and Mapping - HW3: Path Planning - HW4: Robotics Manipulation - HW5: Ontology-based Data Quality Verification Course Project (per group, 2-3 people a group): 50%

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1. Stuart Russell and Peter Norvig (2020). Artificial Intelligence: A Modern Approach, 4th Global ed., Pearson College. 2. Kevin Lynch and Frank Park (2017), Modern Robotics Mechanics, Planning, and Control, Cambridge University Press. 3. Timothy D. Barfoot (2024), State Estimation for Robotics, Second Edition, Cambridge University Press. 4. Keet, C. M. (2025). An introduction to ontology engineering (2nd ed.). College Publications. 5. Allemang, D. (2020). Semantic Web for the Working Ontologist : Effective Modeling for Linked Data, RDFS, and OWL / (Third Edition). Association for Computing Machinery. https://doi.org/10.1145/3382097

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