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<article class="md-content__inner md-typeset">
<a href="https://github.com/pkuflyingpig/cs-self-learning/edit/master/docs/机器学习进阶/roadmap.md" title="Edit this page" class="md-content__button md-icon">
<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20.71 7.04c.39-.39.39-1.04 0-1.41l-2.34-2.34c-.37-.39-1.02-.39-1.41 0l-1.84 1.83 3.75 3.75M3 17.25V21h3.75L17.81 9.93l-3.75-3.75L3 17.25z"/></svg>
</a>
<h1 id="_1">机器学习进阶</h1>
<p>此路线图适用于已经学过了基础机器学习 (ML, NLP, CV, RL) 的同学 (高年级本科生或低年级研究生)，已经发表过至少一篇顶会论文 (NeurIPS, ICML, ICLR, ACL, EMNLP, NAACL, CVPR, ICCV) 想要走机器学习科研路线的选手。</p>
<p>此路线的目标是为读懂与发表机器学习顶会论文打下理论基础，特别是 Probabilistic Methods 这个 track 下的文章</p>
<p>机器学习进阶可能存在多种不同的学习路线，此路线只能代表作者 <a href="https://franxyao.github.io/">Yao Fu</a> 所理解的最佳路径，侧重于贝叶斯学派下的概率建模方法，也会涉及到各项相关学科的交叉知识。</p>
<h2 id="_2">必读教材</h2>
<ul>
<li>PRML: Pattern Recognition and Machine Learning. Christopher Bishop</li>
<li>经典贝叶斯学派教材</li>
<li>AoS: All of Statistics. Larry Wasserman</li>
<li>经典频率学派教材</li>
</ul>
<p>所以这两本书刚好相辅相成</p>
<h2 id="_3">字典</h2>
<ul>
<li>MLAPP: Machine Learning: A Probabilistic Perspective. Kevin Murphy</li>
<li>Convex Optimization. Stephen Boyd and Lieven Vandenberghe</li>
</ul>
<h2 id="_4">进阶书籍</h2>
<ul>
<li>W&amp;J: Graphical Models, Exponential Families, and Variational Inference. Martin Wainwright and Michael Jordan</li>
<li>Theory of Point Estimation. E. L. Lehmann and George Casella</li>
</ul>
<h2 id="_5">如何阅读</h2>
<h3 id="guidelines">Guidelines</h3>
<ul>
<li>必读教材就是一定要读的教材</li>
<li>字典的意思是，一般情况下不管它，但当遇到了不懂的概念的时候，就去字典里面查（而不是维基百科）</li>
<li>进阶书籍先不读，先读完必读书籍。必读书籍一般都是要前前后后反复看过 N 遍才算读完</li>
<li>读的过程中，最重要的读法就是对比阅读 (contrastive-comparative reading)：同时打开两本书讲同一主题的章节，然后对比相同点和不同点和联系</li>
<li>读的过程中，尽量去回想之前读过的论文，比较论文和教材的相同点与不同点</li>
</ul>
<h3 id="_6">基础路径</h3>
<ul>
<li>先读 AoS 第六章: Models, Statistical Inference and Learning，这一部分是最基础的科普</li>
<li>然后读 PRML 第 10, 11 章</li>
<li>第 10 章的内容是 Variational Inference, 第 11 章的内容是 MCMC, 这两种方法是贝叶斯推断的两条最主要路线</li>
<li>如果在读 PRML 的过程中发现有任何不懂的名词，就去翻前面的章节。很大概率能够在第 3，4 章找到相对应的定义；如果找不到或者不够详细，就去查 MLAPP</li>
<li>AoS 第 8 章 (Parametric Inference) 和第 11 章 (Bayesian Inference) 也可以作为参考。最好的方法是多本书对比阅读，流程如下<ul>
<li>假设我在读 PRML 第 10 章的时候发现了一个不懂的词：posterior inference </li>
<li>于是我往前翻，翻到了第 3 章 (Linear Model for Regression)，看到了最简单的 posterior </li>
<li>然后我接着翻 AoS，翻到了第 11 章，也有对 posterior 的描述</li>
<li>然后我对比 PRML 第 10 章，第 3 章，AoS 第 11 章，三处不同地方对 posterior 的解读，比较其相同点和不同点和联系</li>
</ul>
</li>
<li>读完 PRML 第 10 和 11 章之后，接着读 AoS 第 24 章 (Simulation Methods)，然后把它和 PRML 第 11 章对比阅读 -- 这俩都是讲 MCMC</li>
<li>如果到此处发现还有基础概念读不懂，就回到 PRML 第 3 章，把它和 AoS 第 11 章对比阅读</li>
<li>Again，对比阅读非常重要，一定要把不同本书的类似内容同时摆在面前相互对比，这样可以显著增强记忆</li>
<li>然后读 PRML 第 13 章（跳过第 12 章），这一章可以和 MLAPP 的第 17, 18 章对比阅读</li>
<li>MLAPP 第 17 章是 PRML 第 13.2 章的详细版，主要讲 HMM</li>
<li>MLAPP 第 18 章是 PRML 第 13.3 章的详细版，主要讲 LDS</li>
<li>读完 PRML 第 13 章之后，再去读 PRML 第 8 章 (Graphical Models) -- 此时这部分应该会读得很轻松</li>
<li>以上的内容可以进一步对照 CMU 10-708 PGM 课程材料</li>
</ul>
<p>到目前为止，应该能够掌握
- 概率模型的基础定义
- 精准推断 - Sum-Product
- 近似推断 - MCMC
- 近似推断 - VI</p>
<p>然后就可以去做更进阶的内容</p>
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