第一课 introduction

http://cs229.stanford.edu/info.html

Course Description

This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include: supervised learning (generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines); unsupervised learning (clustering, dimensionality reduction, kernel methods); learning theory (bias/variance tradeoffs; VC theory; large margins); reinforcement learning and adaptive control. The course will also discuss recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing.

这门课主要讲机器学习和統計模式識別。主题包括：监督学习（生成/判别学习，参数/非参数学习，神经网络，支持向量机）;无监督学习（聚类，降维，内核的方法）;学习理论（偏差/方差权衡; VC理论;大边距）;强化学习和自适应控制。该课程还将讨论最近的机器学习的应用，如机器人控制，数据挖掘，自主导航，生物信息学，语音识别和文本和网络数据的处理。

Course Materials

The following books are recommended as optional reading:

1. Richard Duda, Peter Hart and David Stork, Pattern Classification, 2nd ed. John Wiley & Sons, 2001.
2. Tom Mitchell, Machine Learning. McGraw-Hill, 1997.
3. Richard Sutton and Andrew Barto, Reinforcement Learning: An introduction. MIT Press, 1998
4. Trevor Hastie, Robert Tibshirani and Jerome Friedman, The Elements of Statistical Learning. Springer, 2009

Course handouts and other materials can be downloaded from :
http://www.stanford.edu/class/cs229/materials.html

Online Resources

• Home page: http://cs229.stanford.edu/
• Current quarter's class videos: Available here for SCPD students and here for non-SCPD students
• Piazza forum: http://piazza.com/stanford/fall2016/cs229
• Staff mailing list: [email protected]
  NOTE: If sending email about a homework, please state in the subject line which assignment and which question the email refers to (e.g., Subject: Hwk3 Q1). Please send one question per email. If you have a technical or homework or general administrative question that is not confidential or personal, we strongly encourage you to post it on the Piazza forum instead, as that will get you a faster response; make a private note if necessary