Lecture 1 | Machine Learning (Stanford)
11 人推荐 · 跨 11 个月 · 2008–2020
中文导读
吴恩达（Andrew Ng）在斯坦福大学主讲的经典机器学习名课 CS229 第一讲。全面勾勒了整门课程的框架体系，涵盖监督学习、无监督学习、学习理论（Learning Theory）以及强化学习的核心版图。
为什么 HN 推它
相比后来风靡全球的 Coursera 普及版，这套原汁原味的斯坦福原版课程数学推导更严密、理论硬度更足，被 HN 网友誉为啃下机器学习底层的黄金经典。
「我强烈建议直接去学油管上的斯坦福原版视频和配套课件。」HN 网友原话：I'd suggest going back to the original youtube[1] and course materials[2].
英文原摘要（HackerTube / Claude 整理）
The first lecture of Andrew Ng's Stanford CS229 Machine Learning course, giving an overview of the course. It introduces supervised and unsupervised learning, learning theory and reinforcement learning.
Recommended as the rigorous, math-heavy original course, deeper than the Coursera version, for learning machine learning.
HN 上的提及
Stanford class on machine learning by Andrew Ng, 20 lectures.
You can also look at Andrew Ng's video lectures
which you can learn using the Andrew Ng's Stanford lectures
Part of a suggested ML study path
presenting the mathematics behind some of the more popular algorithms