The Hopfield model, originally inspired by spin-glass physics, occupies a central place at the intersection of statistical mechanics, neural networks, and modern artificial intelligence. Despite its conceptual simplicity and broad applicability -- from associative memory to near-optimal solutions of combinatorial optimization problems -- it is rarely integrated into standard undergraduate physics curricula. In this paper, we present the Hopfield model as a pedagogically rich framework that naturally unifies core topics from undergraduate statistical physics, dynamical systems, linear algebra, and computational methods. We provide a concise and illustrated theoretical introduction grounded in familiar physics concepts, analyze the model's energy function, dynamics, and pattern stability, and discuss practical aspects of simulation, including a freely available simulation code. To support instruction, we conclude with classroom-ready example problems designed to mirror research practice. By explicitly connecting fundamental physics to contemporary AI applications, this work aims to help prepare physics students to understand, apply, and critically engage with the computational tools increasingly central to research, industry, and society.

翻译：Hopfield模型最初受自旋玻璃物理学的启发，在统计力学、神经网络和现代人工智能的交叉领域占据核心地位。尽管其概念简洁且应用广泛——从联想记忆到组合优化问题的近似最优解——该模型却很少被纳入标准的本科物理课程。本文提出将Hopfield模型作为一个教学内涵丰富的框架，它自然地整合了本科统计物理、动力系统、线性代数和计算方法的核心主题。我们基于熟悉的物理学概念提供了简明且配有图示的理论介绍，分析了模型的能量函数、动力学和模式稳定性，并讨论了仿真的实践要点（包括一份免费提供的仿真代码）。为支持教学，我们最后设计了可直接用于课堂的示例问题，这些问题旨在反映研究实践。通过明确地将基础物理学与当代人工智能应用联系起来，本研究旨在帮助物理专业学生理解、应用并批判性地参与这些日益成为研究、产业和社会核心的计算工具。