论文标题
加速器物理的机器学习简介
Introduction to Machine Learning for Accelerator Physics
论文作者
论文摘要
这对CAS讲座为加速器物理学生提供了介绍机器学习（ML）的框架和术语。我们首先通过线性回归的简单示例引入ML的语言，包括概率的观点，以介绍最大似然估计（MLE）和最大先验（MAP）估计的概念。然后，我们将概念应用于神经网络和逻辑回归的示例。接下来，我们介绍非参数模型和内核方法，并简要介绍了其他两个机器学习范式，即无监督和强化学习。最后，我们在自由电子激光器上使用ML的示例应用程序结束。
This pair of CAS lectures gives an introduction for accelerator physics students to the framework and terminology of machine learning (ML). We start by introducing the language of ML through a simple example of linear regression, including a probabilistic perspective to introduce the concepts of maximum likelihood estimation (MLE) and maximum a priori (MAP) estimation. We then apply the concepts to examples of neural networks and logistic regression. Next we introduce non-parametric models and the kernel method and give a brief introduction to two other machine learning paradigms, unsupervised and reinforcement learning. Finally we close with example applications of ML at a free-electron laser.
