机器学习的前期入门汇总

机器学习机器学习是近20多年兴起的一门多领域交叉学科，涉及几率论、统计学、逼近论、凸分析、算法复杂度理论等多门学科。机器学习理论主要是设计和分析一些让计算机能够自动“学习”的算法。机器学习算法是一类从数据中自动分析得到规律，并利用规律对未知数据进行预测的算法。由于学习算法中涉及了大量的统计学理论，机器学习与统计推断学联系尤其密切，也被称为统计学习理论。算法设计方面，机器学习理论关注能够实现的，行之有效的学习算法。

大体分三类： 起步体悟，实战笔记，行家导读

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

• 有没有作机器学习的哥们？可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，尤为要看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”

• Machine Learning Roadmap: Your Self-Study Guide to Machine Learning (2014) Jason Brownlee -- [行家导读] 虽然是英文版，但很是容易读懂。对Beginner,Novice,Intermediate,Advanced读者都有覆盖。

• A Tour of Machine Learning Algorithms （2013） 这篇关于机器学习算法分类的文章也很是好

• 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

• 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，你不懂的大可能是专业名词，未来不论写文章仍是读文档都是英文为主
  复制代码

机器学习入门资源不彻底汇总更多攻略

• 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)

  
  

机器学习入门资源不彻底汇总课程资源

Tom Mitchell 和 Andrew Ng 的课都很适合入门

机器学习入门资源不彻底汇总入门课程机器学习入门资源不彻底汇总2011Tom Mitchell(CMU)机器学习

英文原版视频与课件PDF他的《机器学习》在不少课程上被选作教材，有中文版。

• 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

机器学习与数据挖掘的区别

• 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 differencebetween 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.

几篇高屋建瓴的机器学习领域概论, 参见原文

• The Discipline of Machine LearningTom Mitchell 当年为在CMU创建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，也许入门时不少概念还不明白，上完公开课后必定要再读一遍。

几本好书

• 李航博士的《统计学习方法》。

1. 数学基础

机器学习必要的数学基础主要包括：多元微积分，线性代数

1. Calculus: Single Variable | Calculus One （可选）

2. Multivariable Calculus

3. Linear Algebra

4. 统计基础

   1. Introduction to Statistics: Descriptive Statistics

   2. Probabilistic Systems Analysis and Applied Probability | 几率 ( 可选)

   3. Introduction to Statistics: Inference

5. 编程基础

   1. Programming for Everybody (Python)

   2. DataCamp: Learn R with R tutorials and coding challenges(R)

   3. Introduction to Computer Science:Build a Search Engine & a Social Network

6. 机器学习

   1. Statistical Learning(R)

   2. Machine Learning

   3. 机器学习基石

   4. 机器学习技法

下面是近期的给外行人读的泛数学科普书籍，由浅至深，做用除了感觉数学之美以外，更重要的是能够做用天天学习的鸡血，由于这些书都比较好读……

1.《数学之美》做者：吴军 2.《 Mathematician's Lament | 数学家的叹息》做者：by Paul Lockhart 3.《 Think Stats: Probability and Statistics forProgrammers | 统计思惟：程序员数学之几率统计 》 做者：Allen B. Downey 4.《 A History of Mathematics | 数学史 》做者：Carl B. Boyer 5.《 Journeys Through Genius | 天才引导的历程：数学中的伟大定理 》做者：William Dunham 6.《 The Mathematical Experience | 数学经验 》做者 Philip J.Davis、Reuben Hersh 7.《 Proofs from the Book | 数学天书中的证实 》做者：Martin Aigner、Günter M. Ziegler 8.《 Proofs and Refutations | 证实与反驳－数学发现的逻辑 》做者：Imre Lakatos

1. Python/C++/R/Java - you will probably want to learnall of these languages at some point if you want a job in machine-learning.Python's Numpy and Scipy libraries [2] are awesome because they have similarfunctionality to MATLAB, but can be easily integrated into a web service andalso used in Hadoop (see below). C++ will be needed to speed code up. R [3] isgreat for statistics and plots, and Hadoop [4] is written in Java, so you mayneed to implement mappers and reducers in Java (although you could use ascripting language via Hadoop streaming [5])

首先，你要熟悉这四种语言。Python由于开源的库比较多，能够看看Numpy和Scipy这两个库，这两个均可以很好的融入网站开发以及Hadoop。C++可让你的代码跑的更快，R则是一个很好地统计工具。而你想很好地使用Hadoop你也必须懂得java，以及如何实现map reduce

2. Probability and Statistics: A good portion oflearning algorithms are based on this theory. Naive Bayes [6], Gaussian MixtureModels [7], Hidden Markov Models [8], to name a few. You need to have a firmunderstanding of Probability and Stats to understand these models. Go nuts andstudy measure theory [9]. Use statistics as an model evaluation metric:confusion matrices, receiver-operator curves, p-values, etc.

我推荐统计学习方法 李航写的，这算的上我mentor的mentor了。理解一些几率的理论，好比贝叶斯，SVM，CRF，HMM，决策树，AdaBoost，逻辑斯蒂回归，而后再稍微看看怎么作evaluation 好比P R F。也能够再看看假设检验的一些东西。

3. Applied Math + Algorithms: For discriminatemodels like SVMs [10], you need to have a firm understanding of algorithmtheory. Even though you will probably never need to implement an SVM fromscratch, it helps to understand how the algorithm works. You will need tounderstand subjects like convex optimization [11], gradient decent [12],quadratic programming [13], lagrange [14], partial differential equations [15],etc. Get used to looking at summations [16].

机器学习毕竟是须要极强极强数学基础的。我但愿开始能够深刻的了解一些算法的本质，SVM是个很好的下手点。能够今后入手，看看拉格朗日，凸优化都是些什么

4. Distributed Computing: Most machine learningjobs require working with large data sets these days (see Data Science) [17].You cannot process this data on a single machine, you will have to distributeit across an entire cluster. Projects like Apache Hadoop [4] and cloud serviceslike Amazon's EC2 [18] makes this very easy and cost-effective. Although Hadoopabstracts away a lot of the hard-core, distributed computing problems, youstill need to have a firm understanding of map-reduce [22], distribute-filesystems [19], etc. You will most likely want to check out Apache Mahout [20]and Apache Whirr [21].

熟悉分布计算，机器学习当今必须是多台机器跑大数据，要否则没啥意义。请熟悉Hadoop，这对找工做有很大很大的意义。百度等公司都须要hadoop基础。

5. Expertise in Unix Tools: Unless you are veryfortunate, you are going to need to modify the format of your data sets so theycan be loaded into R,Hadoop,HBase [23],etc. You can use a scripting languagelike python (using re) to do this but the best approach is probably just masterall of the awesome unix tools that were designed for this: cat [24], grep [25],find [26], awk [27], sed [28], sort [29], cut [30], tr [31], and many more.Since all of the processing will most likely be on linux-based machine (Hadoopdoesnt run on Window I believe), you will have access to these tools. Youshould learn to love them and use them as much as possible. They certainly havemade my life a lot easier. A great example can be found here [1].

熟悉Unix的Tool以及命令。百度等公司都是依靠Linux工做的，可能如今依靠Windows的Service公司已经比较少了。因此怎么也要熟悉Unix操做系统的这些指令吧。我记得有个百度的面试题就是问文件复制的事情。

6. Become familiar with the Hadoop sub-projects:HBase, Zookeeper [32], Hive [33], Mahout, etc. These projects can help youstore/access your data, and they scale.

机器学习终究和大数据息息相关，因此Hadoop的子项目要关注，好比HBase Zookeeper Hive等等

7. Learn about advanced signal processing techniques:feature extraction is one of the most important parts of machine-learning. Ifyour features suck, no matter which algorithm you choose, your going to seehorrible performance. Depending on the type of problem you are trying to solve,you may be able to utilize really cool advance signal processing algorithmslike: wavelets [42], shearlets [43], curvelets [44], contourlets [45], bandlets[46]. Learn about time-frequency analysis [47], and try to apply it to yourproblems. If you have not read about Fourier Analysis[48] and Convolution[49],you will need to learn about this stuff too. The ladder is signal processing101 stuff though.

这里主要是在讲特征的提取问题。不管是分类（classification）仍是回归（regression）问题，都要解决特征选择和抽取（extraction）的问题。他给出了一些基础的特征抽取的工具如小波等，同时说须要掌握傅里叶分析和卷积等等。这部分我不大了解，大概就是说信号处理你要懂，好比傅里叶这些。。。

Finally, practice and read as much as you can. In yourfree time, read papers like Google Map-Reduce [34], Google File System [35],Google Big Table [36], The Unreasonable Effectiveness of Data [37],etc Thereare great free machine learning books online and you should read those also.[38][39][40]. Here is an awesome course I found and re-posted on github [41].Instead of using open source packages, code up your own, and compare theresults. If you can code an SVM from scratch, you will understand the conceptof support vectors, gamma, cost, hyperplanes, etc. It's easy to just load somedata up and start training, the hard part is making sense of it all.

总之机器学习若是想要入门分为两方面： 一方面是去看算法，须要极强的数理基础（真的是极强的），从SVM入手，一点点理解。 另外一方面是学工具，好比分布式的一些工具以及Unix。

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