系统梳理机器学习基础，助新手快速入门核心概念与方法。
Machine Learning: a Lecture Note
- 从分类任务出发，讲解损失函数、反向传播与梯度下降等核心机制。
- 深入探讨生成模型，涵盖GAN、自回归模型与概率图模型等关键技术。
- 适合初学机器学习的硕士与博士生，为后续研究打下坚实基础。
本讲义旨在为数据科学及相关领域的一年级硕士与博士生提供机器学习的基础知识。内容从现代机器学习的基本理念入手，以分类为主要目标任务，涵盖损失函数设计、反向传播、随机梯度下降、泛化能力、模型选择以及人工神经网络的基本构成模块。在此基础上，深入讲解无监督学习的概率方法，包括有向潜在变量模型、专家乘积、生成对抗网络（GAN）和自回归模型。最后，简要介绍强化学习、集成方法与元学习等多样化进阶主题。阅读本讲义后，学生应具备进一步学习和研究更高级机器学习与人工智能课题的能力。
原文摘要 · Abstract (English)
This lecture note is intended to prepare early-year master's and PhD students in data science or a related discipline with foundational ideas in machine learning. It starts with basic ideas in modern machine learning with classification as a main target task. These basic ideas include loss formulation, backpropagation, stochastic gradient descent, generalization, model selection as well as fundamental blocks of artificial neural networks. Based on these basic ideas, the lecture note explores in depth the probablistic approach to unsupervised learning, covering directed latent variable models, product of experts, generative adversarial networks and autoregressive models. Finally, the note ends by covering a diverse set of further topics, such as reinforcement learning, ensemble methods and meta-learning. After reading this lecture note, a student should be ready to embark on studying and researching more advanced topics in machine learning and more broadly artificial intelligence.
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