Lifelong Learn: Machine Learning¶

Books¶

• Introduction to Data Mining

  数据挖掘导论

  中文书名为《数据挖掘导论》，翻译的是第一版，现在又出了英文第二版。内容比较丰富，比较适合入门（因为书籍并没有刻意避开复杂的地方，所以也并没有很简单）。

• Learning From Data

  高屋建瓴之神作

• Chapman & Hall/CRC Data Mining and Knowledge Discovery Series

  Data Mining技术的历史演进

  此系列包括了CRC Press自2007年以来的数据挖掘方面的书籍，从最初的EXCEL, 到SAS，再到Python和R，进行着明显的演进。

Courses¶

• Machine Learning(NYU, DS-GA 1003)

  非常好的Machine Learning课程, 力荐!

  这门课程没有回避任何问题，对优化问题作出了很好的解释和说明。而且课程是站在一个相当高的角度(基于Risk)来展开，很有启发性。课程资料及自己的习题解答放在Github: NYU-ML

• Introduction to Data-Centric AI

  DCAI

  Typical machine learning classes teach techniques to produce effective models for a given dataset. In real-world applications, data is messy and improving models is not the only way to get better performance. You can also improve the dataset itself rather than treating it as fixed. Data-Centric AI (DCAI) is an emerging science that studies techniques to improve datasets, which is often the best way to improve performance in practical ML applications. While good data scientists have long practiced this manually via ad hoc trial/error and intuition, DCAI considers the improvement of data as a systematic engineering discipline.

  This is the first-ever course on DCAI. This class covers algorithms to find and fix common issues in ML data and to construct better datasets, concentrating on data used in supervised learning tasks like classification. All material taught in this course is highly practical, focused on impactful aspects of real-world ML applications, rather than mathematical details of how particular models work. You can take this course to learn practical techniques not covered in most ML classes, which will help mitigate the “garbage in, garbage out” problem that plagues many real-world ML applications.

• Introduction To Machine Learning

  高屋建瓴

  这是根据Understanding Machine Learning: From Theory to Algorithms 这本书找到的课程。

  这本书只看了开头几章，真的是高屋建瓴！而且对PAC理论的推导是我目前见过最为精彩而且容易懂的，所以打算把DS-GA 1003的完成度作为目标，配合课程，书籍，习题和答案将这learning theory这部分的理论补充起来！

• Introduction to Machine Learning(CMU, 10-301 + 10-601)

  Introduction级别好课程

  CMU的ML课程。对我而言比较熟悉的算法，比如Decision Tree,看这个课的lecture notes就感觉略简单了。但是一些不是很熟悉的算法如Reinforcement Learning这些，看这种Introduction级别的还是挺不错的。

• Mining Massive Data Sets(Stanford, CS246, Winter 2020)

  MMDS

  聚焦算法的大规模工程部署, 很值得学的课程

• Machine Learning & Data Mining(Caltech, CS 155, Winter 2020)

  Caltech机器学习

  Lecture notes看着还不错

• CS 189/289A-Introduction to Machine Learning

  Berkeley统计机器学习

  理论讲的比较深入，给了很多有用的资料。（后面需要重点看的）

• Foundations of Machine Learning-Fall2020

  硬核ML Foundation

  书籍Foundations of Machine Learning作者开设的对应的课程

• Machine Learning and Data Mining-CSC 411 Winter 2019

  Toronto大学的机器学习课程

  选录进入此系列是因为这个课程覆盖面较全，虽然较为精简。然后也有配套的homework。