Machine Learning分类总结 和机器学习的四个等级
两个网站：推荐http://www.mlsurveys.com/
Machine Learning分类总结
A list of literature surveys, reviews, and tutorials on Machine Learning and related topics
| ilistic Topic Models | 2012 | Natural Language Processing | |
| Ensemble Approaches for Regression: a Survey | 2012 | Regression | |
| Ontology Learning From Text: A Look Back And Into The Future | 2012 | Natural Language Processing | |
| Pedestrian Detection: An Evaluation of the State of the Art | 2012 | Computer Vision | |
| Time-Series Data Mining | 2012 | Time Series | |
| A Survey of Emerging Approaches to Spam Filtering | 2012 | Applications | |
| A Comparative Study of Palmprint Recognition Algorithms | 2012 | Computer Vision | |
| Representation Learning: A Review and New Perspectives | 2012 | Deep Learning | |
| A Few Useful Things to Know about Machine Learning | 2012 | Machine Learning | |
| Support Vector Machines in Bioinformatics: a Survey | 2012 | Biology |
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Level-Up Your Machine Learning
http://metacademy.org/roadmaps/cjrd/level-up-your-ml
What should I do if I want to get 'better' at machine learning, but I don't know what I want to learn?
Excellent question! My answer: consistently work your way through textbooks.
I then watch as they grimace in the same way an out-of-shape person grimaces when a healthy friend responds with, "Oh, I watch what I eat and consistently exercise." Progress requires consistent discipline, motivation, and an ability to work through challenges on your own. But you already know this.
但是，为什么教科书？因为他们是为数不多的学习媒介在这里您可以真正拥有的知识之一。你可以把一课程，MOOC，参加读书会，任何你想要的。但课本这是一个亲密的纽带。你会波及脑汁在每一页上;你会在不经意间记住的章节标题，例子和练习;你会涂抹在利润和狗的耳朵经常引用的地区，并期待为你学习的主题应用 - 教科书本身变成自己的知识的一部分（上图显示了我最近的教科书）。成功的学习者不只是读课本。学会使用教材以这种方式，你可以掌握很多科目 - 当然机器学习。
In this brief roadmap, I list a few excellent textbooks for advancing your machine learning knowledge and capabilities. I picked these texts after consulting with fellow graduates students, postdocs, and professors at UC Berkeley -- my own experience played a role as well. This list is purposefully sparse. Having 20 textbooks thrown at you is useless.
在这个简短的路线图，我列出了几个优秀的教科书为推进你的机器学习的知识和能力。我自己的经验发挥了作用，以及 - I咨询与其他毕业生的学生，博士后，教授和加州大学伯克利分校后，捞起这些文本。这个名单是故意稀疏。有20课本扔向你是无用的。
Also, if you want alternative learning resources, Metacademy is at your disposal as are all of these textbooks
.Level 0: Neophyte(新手)
Data Smart: Using Data Science to Transform Information into Insight
My sister, an artist and writer by trade, asked me how she could understand the basics of data science in a nontrivial way. After reading several introductory and pop books in this area, I recommended Data Smart. My sister was able to work through it, and in fact, the next time I saw her we had a delightful conversation about logistic regression =).
我的姐姐，艺术家和作家通过贸易，问我她怎么能理解数据的科学的基础知识的非平凡的方式。阅读一些介绍和流行的书在这个领域之后，我推荐数据智能。我的姐姐是能够工作，通过它，事实上，下一次我看见她，我们有大约回归一个愉快的对话=）。
Expectations: You'll understand some common machine learning algorithms at a high-level, and you'll be able to implement some simple algorithms in Excel (and a bit in R if you get through the entire book).
期望：你会了解一些常见的机器学习算法在一个较高的层次，你就可以实现在Excel中一些简单的算法（和位在研究如果通过整本书拿到）。
Necessary Background: basic Excel familiarity -- this book is a great starting point if you don’t have a CS/math-based background. Plus, it's not nearly as dry as a typical textbook.
必要的背景知识：基本的Excel熟悉 - 这本书是，如果你没有一个CS /数学系背景的一个很好的起点。此外，它几乎没有干成一个典型的教科书。
Key Chapters: It's a short read, and every chapter is fairly illuminating -- though, you can skip the worksheet examples, and chapters 8 and 10 if you're interested in a basic overview.
关键章：这是一个简短的阅读，每章都相当启发性 - 不过，你可以跳过该工作表的例子，和第8章和第10，如果你有兴趣的基本概况。
Capstone Project: Using this dataset see if you can predict the MPG of the car given all of its other attributes. This will test your ability to manipulate data for a desired machine learning task, and also your ability to apply the correct machine learning technique to a somewhat vague problem.
凯普斯项目：使用该数据集看你能不能预测汽车给它的所有其他属性的省油。这将考验你的能力来操纵的所需机器学习任务数据，也你到正确的机器学习技术应用到有些模糊问题的能力。
Level 1: Apprentice（学徒）
Machine Learning with R
This is an example-laden book for simultaneously learning practical machine learning techniques and the R programming language. I'm a long time Scipy user, but after finishing the first few chapters (and remembering that R packages are so damn simple), I've mostly been turning to R for quick analyses.
Expectations: You'll be able to recognize when fundamental machine learning algorithms apply to certain problems and implement functioning machine learning code in R
这是一个例子，载货书同时学习实用机器学习技术及R编程语言。我是一个很长的时间SciPy的用户，但在完成前几章（和记忆的R套件是如此该死的简单）后，我大部分时间被转向至R快速分析。
期望值：你能够认识到，当基本的机器学习算法应用到的一些问题，并落实在研究工作的机器学习代码
Necessary Background: No real prerequisites, though the following will help (these can be learned/reviewed as you go):
- some programming experience [in R]
- some algebra
- basic calculus
- a little bit of probability theory
Key Chapters: It's a short book, and I recommend all of the chapters -- be sure to actually think through the examples (and type them into R). If you're looking to shave off some time, you can safely skip chapters 8 and 12.
Capstone Project: Using this dataset see if you can predict the food ratings given all of the other attributes. Use three different machine learning techniques for this task, and justify your top choice. Also, build a classifier that predicts whether a review is "good" or "bad" -- you should use reasonable "good/bad" thresholds. This will test your data munging capabilities, your strategy for analyzing a larger dataset, your knowledge of machine learning techniques, and your ability to write analysis code in R.
钥章：这是一本小书，我建议所有的章节 - 一定要真正想通过实例（以及它们输入到R）。如果你正在寻找剃掉了一段时间，你可以跳过第8章和第12。
凯普斯项目：使用该数据集看看您是否可以预测给定的所有其他属性的食物收视率。使用三种不同的机器学习技术为这项任务，并证明你的首选。此外，建立一个分类器，预测检讨是否为“好”或“坏”的 - 你应该使用合理的“好/坏”阈值。这将考验你的数据munging能力，你的战略，分析一个更大的数据集，您的机器学习技术知识，和你写的代码分析，在河的能力
Level 2: Journeyman（熟练工）
This stage separates those with a surface-level understanding from those with rigorous, in-depth, knowledge. It starts getting mathy at this stage, but if you plan on making machine learning a substantial part of your career, you'll have to cross this bridge. PRML is the classic bridge. Use it. Read it. Love it. But keep in mind that a Bayesian perspective isn't the only story (Bishop strongly tends towards the Bayesian approach to machine learning).
这一阶段的分离与那些严谨，深入，知识的表面层次的理解。它开始变得MATHY在这个阶段，但如果你打算使机器学习你的职业生涯的很大一部分，你必须跨过这道坎。 PRML是经典的桥梁。使用它。读它。爱它。但是，请记住，贝叶斯观点并不是唯一的故事（主教强烈趋向贝叶斯方法的机器学习）。
Expectations Be able to recognize, implement, debug, and interpret the output of most off-the-shelf machine learning methods. Also, you should have an intuition about which advanced ML concepts to investigate for a given problem. Practicing data scientists should at least be at this level.
期望能够认识到，实施，调试，并解释大部分关闭的，现成的机器学习方法的输出。此外，你应该有一个直觉的先进ML概念，调查一个给定的问题。练数据科学家至少应该在这个水平。
Necessary Background:
- you should be comfortable with off-the-shelf clustering and classification algorithms
- linear algebra: understand matrix algebra and determinants
- some multivariate and vector calculus experience -- know what a Jacobian is
- some machine learning implementation experience in R, Matlab, the SciPy stack, or Julia.
- 你应该熟悉了的，现成的聚类和分类算法
- 线性代数：理解矩阵代数和决定因素
- 一些多元和矢量微积分的经验 - 知道什么是雅可比是
- 在R，MATLAB，在SciPy的堆栈，或朱莉娅一些机器学习的实施经验。
Key Chapters: Know and love chapters 1-12.1. Chapters 12.2 - 14 can be consulted as you need them.
钥章：熟悉和喜爱的章节1-12.1。章12.2 - 14可以根据您的需要进行协商。
Capstone Project: Implement the Online Variational Bayes Algorithm for Latent Dirichlet Allocation and analyze a large corpus of your choosing. Verify that your LDA implementation is correct. This will test your ability to understand and interpret cutting-edge machine learning algorithms, approximate and online inference techniques, as well as your implementation chops, your data munging abilities, and your ability to define an interesting application from a vaguely defined problem.
凯普斯项目：实现在线变分贝叶斯算法隐含狄利克雷分配和分析您所选择的大语料库。请确认您的LDA的实现是正确的。这将考验你的能力，理解和解释前沿的机器学习算法，近似和在线推理技巧，以及你实现印章，你的数据munging的能力，你的定义从定义模糊的问题，一个有趣的应用程序的能力。
Note PRML spends quite a bit of time on Bayesian machine learning methods. If you're unfamiliar with Bayesian statistics, I recommend studying the first 5 chapters of Doing Bayesian Data Analysis
注意PRML花费相当多的时间在贝叶斯机器学习方法。如果你不熟悉贝叶斯统计，我建议做学习的贝叶斯数据分析的前5章
Level 3: Master精通
Probabilistic Graphical Models: Principles and Techniques
There's a number of subjects you may want to study in depth at the master level: convex optimization, [measure-theoretic] probability theory, discrete optimization, linear algebra, differential geometry, or maybe computational neurology. But if you're at this level, you probably have a good sense of what areas you'd like to improve, so I'll stick with the single book recommendation. Probabilistic Graphical Models: Principles and Techniques is a classic, monstrous tomb that should be within arms length of any ML researcher worth his/her salt =). PGMs pervade machine learning, and with a strong understanding of this content, you'll be able to dive into most machine learning specialties without too much pain.
有一些科目可能要深入的大师级研究：凸优化[措施 - 理论]概率论，离散优化，线性代数，微分几何，或者计算神经。但是，如果你在这个层面上，你可能有你想要哪些方面提高一个良好的感觉，所以我会坚持使用单书的建议。概率图模型：原理与技术是一个典型的，可怕的坟墓应该是在任何的ML研究员值得他/她的盐=）武器长度。铂族金属随处可见机器学习，以及与此内容的深入了解，你就能够深入到大多数机器学习专业没有太多的痛苦。
Expectations: You'll be able to construct probabilistic models for novel problems, determine a reasonable inference technique, and evaluate your methodology. You'll also have a much deeper understanding of how various models relate, e.g. how deep belief networks can be viewed as factor graphs.
期望：你可以建立概率模型的新问题，确定一个合理的推断方法，并评估方法论。您还可以有怎样不同的型号联系，例如一个更深刻的认识信念有多深网可看作因子图。
Necessary Background:
- you should be comfortable with most off-the-shelf ML algorithms
- linear algebra -- know how to interpret eigenvalues
- multivariate and vector calculus experience
- some machine learning implementation experience in R, Matlab, the SciPy stack, or Julia.
- 你应该熟悉最现成的现成的ML算法
- 线性代数 - 知道如何解释的特征值
- 多元和矢量微积分的经验
- 在R，MATLAB，在SciPy的堆栈，或朱莉娅一些机器学习的实施经验。
Key Chapters: Chapters 1-8 cover similar content as Bishop's Pattern Recognition and Machine learning Ch. 2 and 8, but at a much deeper level. Chapters 9-13 contain key content, and Ch. 19 on partially observed data is really helpful. Read Ch. 14 and Ch. 15 when/if they are relevant to your goals.
主要章节：章1-8封面相似内容的主教模式识别和机器学习通道。 2和8，但在更深的层次。章9-13包含的重点内容和CH。 19日部分观察到的数据是真正有用的。读取通道。 14和通道。 15当/如果他们与您的目标。
Capstone Project: At this point, you should be able to define and pursue your own machine learning projects. Perhaps plunge into the world of "big data".
凯普斯项目：在这一点上，你应该能够定义和追求自己的机器学习项目。也许陷入了“大数据”的美誉。
Level 4: Grandmaster 宗师
If you've achieved master status, you'll have a strong enough ML background to pursue any ML-related specialization at a novel level: e.g. maybe you're interested in pursuing novel deep learning applications or characterizations? Maybe you should become a Metacademy contributor?
如果你已经取得了主人的地位，你就会有一个足够强大的ML背景，以追求任何ML相关的专业，在一个新的层面：如也许你热衷于追求新颖深刻的学习应用程序或表征？也许你应该成为一个Metacademy贡献者？