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<!doctype html><html lang=zh class=no-js> <head><meta charset=utf-8><meta name=viewport content="width=device-width,initial-scale=1"><meta name=description content=CS自学指南><meta name=author content=PKUFlyingPig><link href=https://csdiy.wiki/%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0%E8%BF%9B%E9%98%B6/roadmap/ rel=canonical><link href=../../%E6%B7%B1%E5%BA%A6%E5%AD%A6%E4%B9%A0/CS285/ rel=prev><link href=../CMU10-708/ rel=next><link rel=icon href=../../images/favicon.ico><meta name=generator content="mkdocs-1.5.3, mkdocs-material-9.5.2"><title>进阶路线图 - CS自学指南</title><link rel=stylesheet href=../../assets/stylesheets/main.50c56a3b.min.css><link rel=stylesheet href=../../assets/stylesheets/palette.06af60db.min.css><link rel=preconnect href=https://fonts.gstatic.com crossorigin><link rel=stylesheet href="https://fonts.googleapis.com/css?family=Roboto+Slab:300,300i,400,400i,700,700i%7CRoboto+Mono:400,400i,700,700i&display=fallback"><style>:root{--md-text-font:"Roboto Slab";--md-code-font:"Roboto 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href=../CS229M/ class=md-nav__link> <span class=md-ellipsis> Stanford STATS214 / CS229M: Machine Learning Theory </span> </a> </li> </ul> </nav> </li> <li class=md-nav__item> <a href=../../%E5%90%8E%E8%AE%B0/ class=md-nav__link> <span class=md-ellipsis> 后记 </span> </a> </li> </ul> </nav> </div> </div> </div> <div class="md-sidebar md-sidebar--secondary" data-md-component=sidebar data-md-type=toc> <div class=md-sidebar__scrollwrap> <div class=md-sidebar__inner> <nav class="md-nav md-nav--secondary" aria-label=目录> <label class=md-nav__title for=__toc> <span class="md-nav__icon md-icon"></span> 目录 </label> <ul class=md-nav__list data-md-component=toc data-md-scrollfix> <li class=md-nav__item> <a href=#_2 class=md-nav__link> <span class=md-ellipsis> 必读教材 </span> </a> </li> <li class=md-nav__item> <a href=#_3 class=md-nav__link> <span class=md-ellipsis> 字典 </span> </a> </li> <li class=md-nav__item> <a href=#_4 class=md-nav__link> <span class=md-ellipsis> 进阶书籍 </span> </a> </li> <li class=md-nav__item> <a href=#_5 class=md-nav__link> <span class=md-ellipsis> 如何阅读 </span> </a> <nav class=md-nav aria-label=如何阅读> <ul class=md-nav__list> <li class=md-nav__item> <a href=#guidelines class=md-nav__link> <span class=md-ellipsis> Guidelines </span> </a> </li> <li class=md-nav__item> <a href=#_6 class=md-nav__link> <span class=md-ellipsis> 基础路径 </span> </a> </li> </ul> </nav> </li> </ul> </nav> </div> </div> </div> <div class=md-content data-md-component=content> <article class="md-content__inner md-typeset"> <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>AoS: All of Statistics. Larry Wasserman</li> </ul> <p>这两本书分别是经典贝叶斯学派和经典频率学派的教材，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</p> <ul> <li>概率模型的基础定义</li> <li>精准推断 - Sum-Product</li> <li>近似推断 - MCMC</li> <li>近似推断 - VI</li> </ul> <p>然后就可以去做更进阶的内容</p> <aside class=md-source-file> <span class=md-source-file__fact> <span class=md-icon title=最后更新> <svg xmlns=http://www.w3.org/2000/svg viewbox="0 0 24 24"><path d="M21 13.1c-.1 0-.3.1-.4.2l-1 1 2.1 2.1 1-1c.2-.2.2-.6 0-.8l-1.3-1.3c-.1-.1-.2-.2-.4-.2m-1.9 1.8-6.1 6V23h2.1l6.1-6.1-2.1-2M12.5 7v5.2l4 2.4-1 1L11 13V7h1.5M11 21.9c-5.1-.5-9-4.8-9-9.9C2 6.5 6.5 2 12 2c5.3 0 9.6 4.1 10 9.3-.3-.1-.6-.2-1-.2s-.7.1-1 .2C19.6 7.2 16.2 4 12 4c-4.4 0-8 3.6-8 8 0 4.1 3.1 7.5 7.1 7.9l-.1.2v1.8Z"/></svg> </span> <span class="git-revision-date-localized-plugin git-revision-date-localized-plugin-date">December 16, 2023</span> </span> </aside> <!-- Insert generated snippet here --> <script src=https://giscus.app/client.js data-repo=PKUFlyingPig/cs-self-learning data-repo-id=R_kgDOGP67ng data-category=Announcements data-category-id=DIC_kwDOGP67ns4COM9Q data-mapping=title data-reactions-enabled=1 data-emit-metadata=0 data-input-position=top data-theme=light_protanopia data-lang=zh-CN data-loading=lazy crossorigin=anonymous async>
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