translate advanced ML

docs/机器学习进阶/CMU10-708.en.md

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+# CMU 10-708: Probabilistic Graphical Models
+
+## Course Introduction
+
+- **University**: Carnegie Mellon University (CMU)
+- **Prerequisites**: Machine Learning, Deep Learning, Reinforcement Learning
+- **Course Difficulty**: 🌟🌟🌟🌟🌟
+- **Course Website**: [CMU 10-708](https://sailinglab.github.io/pgm-spring-2019/)
+- **Course Resources**: The course website includes slides, notes, videos, homework, and project materials.
+
+CMU's course on Probabilistic Graphical Models, taught by Eric P. Xing, is a foundational and advanced course on graphical models. The curriculum covers the basics of graphical models, their integration with neural networks, applications in reinforcement learning, and non-parametric methods, making it a highly rigorous and comprehensive course.
+
+For students with a solid background in machine learning, deep learning, and reinforcement learning, this course provides a deep dive into the theoretical and practical aspects of probabilistic graphical models. The extensive resources available on the course website make it an invaluable learning tool for anyone looking to master this complex and rapidly evolving field.

docs/机器学习进阶/CMU10-708.md

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 - 先修要求：Machine Learning, Deep Learning, Reinforcement Learning
 - 课程难度：🌟🌟🌟🌟🌟
 - 课程网站：<https://sailinglab.github.io/pgm-spring-2019/>
-  - 这个网站包含了所有的资源：slides, nots, video, homework, project
+- 课程网站包含了所有的资源：slides, notes, video, homework, and project
 
-这门课程是 CMU 的图模型基础 + 进阶课，授课老师为 Eric P. Xing，涵盖了图模型基础，与神经网络的结合，在强化学习中的应用，以及非参数方法。相当硬核
+这门课程是 CMU 的图模型基础 + 进阶课，授课老师为 Eric P. Xing，涵盖了图模型基础，与神经网络的结合，在强化学习中的应用，以及非参数方法，相当硬核。

docs/机器学习进阶/CS229M.en.md

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+# STATS214 / CS229M: Machine Learning Theory
+
+## Course Introduction
+
+- **University**: Stanford
+- **Prerequisites**: Machine Learning, Deep Learning, Statistics
+- **Course Difficulty**: 🌟🌟🌟🌟🌟🌟
+- **Course Website**: [STATS214 / CS229M](http://web.stanford.edu/class/stats214/)
+
+This course offers a rigorous blend of classical learning theory and the latest developments in deep learning theory, making it exceptionally challenging and comprehensive. Previously taught by Percy Liang, the course is now led by Tengyu Ma, ensuring a high level of expertise and insight into the theoretical aspects of machine learning. 
+
+The curriculum is designed for students with a solid foundation in machine learning, deep learning, and statistics, aiming to deepen their understanding of the underlying theoretical principles in these fields. This course is an excellent choice for anyone looking to gain a thorough understanding of both the traditional and contemporary theoretical approaches in machine learning.

docs/机器学习进阶/CS229M.md

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 - 课程难度：🌟🌟🌟🌟🌟🌟
 - 课程网站：<http://web.stanford.edu/class/stats214/>
 
-经典学习理论 + 最新深度学习理论，非常硬核。授课老师之前是 Percy Liang，现在是 Tengyu Ma
+经典学习理论 + 最新深度学习理论，非常硬核。授课老师之前是 Percy Liang，现在是 Tengyu Ma。

docs/机器学习进阶/STA4273.en.md

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+# STA 4273 Winter 2021: Minimizing Expectations
+
+## Course Introduction
+
+- **University**: University of Toronto
+- **Prerequisites**: Bayesian Inference, Reinforcement Learning
+- **Course Difficulty**: 🌟🌟🌟🌟🌟🌟🌟
+- **Course Website**: [STA 4273 Winter 2021](https://www.cs.toronto.edu/~cmaddis/courses/sta4273_w21/)
+
+"Minimizing Expectations" is an advanced Ph.D. level research course, focusing on the interplay between inference and control. The course is taught by Chris Maddison, a founding member of AlphaGo and a NeurIPS 2014 best paper awardee.
+
+This course is notably challenging and is designed for students who have a strong background in Bayesian Inference and Reinforcement Learning. The curriculum explores deep theoretical concepts and their practical applications in the fields of machine learning and artificial intelligence.
+
+Chris Maddison's expertise and his significant contributions to the field, particularly in the development of AlphaGo, make this course highly prestigious and insightful for Ph.D. students and researchers looking to deepen their understanding of inference and control in advanced machine learning contexts. The course website provides valuable resources for anyone interested in this specialized area of study.

docs/机器学习进阶/STA4273.md

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 - 课程难度：🌟🌟🌟🌟🌟🌟🌟
 - 课程网站：<https://www.cs.toronto.edu/~cmaddis/courses/sta4273_w21/>
 
-这是一门较为进阶的 Ph.D. 研究课程，核心内容是 inference 和 control 之间的关系。授课老师为 Chris Maddison (AlphaGo founding member, NeurIPS 14 best paper)
+这是一门较为进阶的 Ph.D. 研究课程，核心内容是 inference 和 control 之间的关系。授课老师为 Chris Maddison (AlphaGo founding member, NeurIPS 14 best paper)。

docs/机器学习进阶/STAT8201.en.md

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+# Columbia STAT 8201: Deep Generative Models
+
+## Course Introduction
+
+- **University**: Columbia University
+- **Prerequisites**: Machine Learning, Deep Learning, Graphical Models
+- **Course Difficulty**: 🌟🌟🌟🌟🌟🌟
+- **Course Website**: [STAT 8201](http://stat.columbia.edu/~cunningham/teaching/GR8201/)
+
+"Deep Generative Models" is a Ph.D. level seminar course at Columbia University, taught by John Cunningham. This course is structured around weekly paper presentations and discussions, focusing on deep generative models, which represent the intersection of graphical models and neural networks and are one of the most important directions in modern machine learning.
+
+The course is designed to explore the latest advancements and theoretical foundations in deep generative models. Participants engage in in-depth discussions about current research papers, fostering a deep understanding of the subject matter. This format not only helps students keep abreast of the latest developments in this rapidly evolving field but also sharpens their critical thinking and research skills.
+
+Given the advanced nature of the course, it is ideal for Ph.D. students and researchers who have a solid foundation in machine learning, deep learning, and graphical models, and are looking to delve into the cutting-edge of deep generative models. The course website provides a valuable resource for accessing the curriculum and related materials.

docs/机器学习进阶/STAT8201.md

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 - 课程难度：🌟🌟🌟🌟🌟🌟
 - 课程网站：<http://stat.columbia.edu/~cunningham/teaching/GR8201/>
 
-这门课是一门 PhD 讨论班，每周的内容是展示 + 讨论论文，授课老师是 John Cunningham。Deep Generative Models （深度生成模型） 是图模型与神经网络的结合，也是现代机器学习最重要的方向之一
+这门课是一门 PhD 讨论班，每周的内容是展示 + 讨论论文，授课老师是 John Cunningham。Deep Generative Models （深度生成模型） 是图模型与神经网络的结合，也是现代机器学习最重要的方向之一。

docs/机器学习进阶/roadmap.en.md

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+# Advanced Machine Learning
+
+This learning path is suitable for students who have already learned the basics of machine learning (ML, NLP, CV, RL), such as senior undergraduates or junior graduate students, and have published at least one paper in top conferences (NeurIPS, ICML, ICLR, ACL, EMNLP, NAACL, CVPR, ICCV) and are interested in pursuing a research path in machine learning.
+
+The goal of this path is to lay the theoretical groundwork for understanding and publishing papers at top machine learning conferences, especially in the track of Probabilistic Methods.
+
+There can be multiple advanced learning paths in machine learning, and this one represents the best path as understood by the author [Yao Fu](https://franxyao.github.io/), focusing on probabilistic modeling methods under the Bayesian school and involving interdisciplinary knowledge.
+
+## Essential Textbooks
+
+- PRML: Pattern Recognition and Machine Learning by Christopher Bishop
+- AoS: All of Statistics by Larry Wasserman
+
+These two books respectively represent classic teachings of the Bayesian and frequentist schools, complementing each other nicely.
+
+## Reference Books
+
+- MLAPP: Machine Learning: A Probabilistic Perspective by Kevin Murphy
+- Convex Optimization by Stephen Boyd and Lieven Vandenberghe
+
+## Advanced Books
+
+- W&J: Graphical Models, Exponential Families, and Variational Inference by Martin Wainwright and Michael Jordan
+- Theory of Point Estimation by E. L. Lehmann and George Casella
+
+## Reading Guidelines
+
+### How to Approach
+
+- Essential textbooks are a must-read.
+- Reference books are like dictionaries: consult them when encountering unfamiliar concepts (instead of Wikipedia).
+- Advanced books should be approached after completing the essential textbooks, which should be read multiple times for thorough understanding.
+- Contrastive-comparative reading is crucial: open two books on the same topic, compare similarities, differences, and connections.
+- Recall previously read papers during reading and compare them with textbook content.
+
+### Basic Pathway
+
+1. Start with AoS Chapter 6: Models, Statistical Inference, and Learning as a basic introduction.
+2. Read PRML Chapters 10 and 11:
+   - Chapter 10 covers Variational Inference, and Chapter 11 covers MCMC, the two main routes for Bayesian inference.
+   - Consult earlier chapters in PRML or MLAPP for any unclear terms.
+   - AoS Chapter 8 (Parametric Inference) and Chapter 11 (Bayesian Inference) can also serve as references. Compare these chapters with the relevant PRML chapters.
+3. After PRML Chapters 10 and 11, proceed to AoS Chapter 24 (Simulation Methods) and compare it with PRML Chapter 11, focusing on MCMC.
+4. If foundational concepts are still unclear, review PRML Chapter 3 and compare it with AoS Chapter 11.
+5. Read PRML Chapter 13 (skip Chapter 12) and compare it with MLAPP Chapters 17 and 18, focusing on HMM and LDS.
+6. After completing PRML Chapter 13, move on to Chapter 8 (Graphical Models).
+7. Cross-reference these topics with CMU 10-708 PGM course materials.
+
+By this point, you should have a grasp of:
+
+- Basic definitions of probabilistic models
+- Exact inference - Sum-Product
+- Approximate inference - MCMC
+- Approximate inference - VI
+
+Afterward, you can proceed to more advanced topics.

docs/机器学习进阶/roadmap.md

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 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。
 
-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
+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。
 
 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 [Yao Fu](https://franxyao.github.io/) 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。
 
 ## 必读教材
 
 - PRML: Pattern Recognition and Machine Learning. Christopher Bishop
-  - 经典贝叶斯学派教材
 - AoS: All of Statistics. Larry Wasserman
-  - 经典频率学派教材
 
-所以这两本书刚好相辅相成
+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。
 
 ## 字典