# 机器学习资源

• 机器学习入门者学习指南 @果壳网 (2013) 作者 白马 -- [起步体悟] 研究生型入门者的亲身经历

• 有没有做机器学习的哥们？能否介绍一下是如何起步的 @ourcoders -- [起步体悟] 研究生型入门者的亲身经历，尤其要看reyoung的建议

• tornadomeet 机器学习 笔记 (2013) -- [实战笔记] 学霸的学习笔记，看看小伙伴是怎样一步一步地掌握“机器学习”

• A Tour of Machine Learning Algorithms （2013） 这篇关于机器学习算法分类的文章也非常好
• Best Machine Learning Resources for Getting Started（2013） 这片有中文翻译 机器学习的最佳入门学习资源 @伯乐在线 译者 programmer_lin
• 门主的几个建议

• 既要有数学基础，也要编程实践
• 别怕英文版，你不懂的大多是专业名词，将来不论写文章还是读文档都是英文为主
• [我是小广告][我是小广告]订阅机器学习日报，跟踪业内热点资料。

• 机器学习该怎么入门 @知乎 (2014)
• What's the easiest way to learn machine learning @quora (2013)
• What is the best way to study machine learning @quora (2012)
• Is there any roadmap for learning Machine Learning (ML) and its related courses at CMU Is there any roadmap for learning Machine Learning (ML) and its related courses at CMU(2014)

Tom Mitchell 和 Andrew Ng 的课都很适合入门

• Decision Trees
• Probability and Estimation
• Naive Bayes
• Logistic Regression
• Linear Regression
• Practical Issues: Feature selection，Overfitting ...
• Graphical models: Bayes networks, EM，Mixture of Gaussians clustering ...
• Computational Learning Theory: PAC Learning, Mistake bounds ...
• Semi-Supervised Learning
• Hidden Markov Models
• Neural Networks
• Learning Representations: PCA, Deep belief networks, ICA, CCA ...
• Kernel Methods and SVM
• Active Learning
• Reinforcement Learning 以上为课程标题节选

1. Introduction (Week 1)
2. Linear Regression with One Variable (Week 1)
3. Linear Algebra Review (Week 1, Optional)
4. Linear Regression with Multiple Variables (Week 2)
5. Octave Tutorial (Week 2)
6. Logistic Regression (Week 3)
7. Regularization (Week 3)
8. Neural Networks: Representation (Week 4)
9. Neural Networks: Learning (Week 5)
10. Advice for Applying Machine Learning (Week 6)
11. Machine Learning System Design (Week 6)
12. Support Vector Machines (Week 7)
13. Clustering (Week 8)
14. Dimensionality Reduction (Week 8)
15. Anomaly Detection (Week 9)
16. Recommender Systems (Week 9)
17. Large Scale Machine Learning (Week 10)
18. Application Example: Photo OCR
19. Conclusion

2013年Yaser Abu-Mostafa (Caltech) Learning from Data -- 内容更适合进阶 课程视频,课件PDF@Caltech

1. The Learning Problem
2. Is Learning Feasible?
3. The Linear Model I
4. Error and Noise
5. Training versus Testing
6. Theory of Generalization
7. The VC Dimension
9. The Linear Model II
10. Neural Networks
11. Overfitting
12. Regularization
13. Validation
14. Support Vector Machines
15. Kernel Methods
17. Three Learning Principles
18. Epilogue

2014年 林軒田(国立台湾大学) 機器學習基石 (Machine Learning Foundations) -- 内容更适合进阶，華文的教學講解 课程主页

When Can Machines Learn? [何時可以使用機器學習] The Learning Problem [機器學習問題] -- Learning to Answer Yes/No [二元分類] -- Types of Learning [各式機器學習問題] -- Feasibility of Learning [機器學習的可行性]

Why Can Machines Learn? [為什麼機器可以學習] -- Training versus Testing [訓練與測試] -- Theory of Generalization [舉一反三的一般化理論] -- The VC Dimension [VC 維度] -- Noise and Error [雜訊一錯誤]

How Can Machines Learn? [機器可以怎麼樣學習] -- Linear Regression [線性迴歸] -- Linear `Soft' Classification [軟性的線性分類] -- Linear Classification beyond Yes/No [二元分類以外的分類問題] -- Nonlinear Transformation [非線性轉換]

How Can Machines Learn Better? [機器可以怎麼樣學得更好] -- Hazard of Overfitting [過度訓練的危險] -- Preventing Overfitting I: Regularization [避免過度訓練一：控制調適] -- Preventing Overfitting II: Validation [避免過度訓練二：自我檢測] -- Three Learning Principles [三個機器學習的重要原則]

2008年Andrew Ng CS229 机器学习 -- 这组视频有些年头了，主讲人这两年也高大上了.当然基本方法没有太大变化，所以课件PDF可下载是优点。 中文字幕视频@网易公开课 | 英文版视频@youtube |课件PDF@Stanford

2012年余凯(百度)张潼(Rutgers) 机器学习公开课 -- 内容更适合进阶 课程主页@百度文库 ｜ 课件PDF@龙星计划

mitbbs.com/bbsdoc/DataS MITBBS－ 电脑网络 - 数据科学版

cos.name/cn/forum/22 统计之都 » 统计学世界 » 数据挖掘和机器学习

josephmisiti/awesome-machine-learning · GitHub 机器学习资源大全

Machine Learning Video Library Caltech 机器学习视频教程库，每个课题一个视频

Analytics, Data Mining, and Data Science 数据挖掘名站

datasciencecentral.com/ 数据科学中心网站

• 机器学习关注从训练数据中学到已知属性进行预测
• 数据挖掘侧重从数据中发现未知属性

Dan Levin, What is the difference between statistics, machine learning, AI and data mining?

• If there are up to 3 variables, it is statistics.
• If the problem is NP-complete, it is machine learning.
• If the problem is PSPACE-complete, it is AI.
• If you don't know what is PSPACE-complete, it is data mining.

• The Discipline of Machine LearningTom Mitchell 当年为在CMU建立机器学习系给校长写的东西。
• A Few Useful Things to Know about Machine Learning Pedro Domingos教授的大道理，也许入门时很多概念还不明白，上完公开课后一定要再读一遍。

• 李航博士的《统计学习方法》一书前段也推荐过，给个豆瓣的链接