sophia-11/machine-learning-notes

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Mindmap

mindmap
  root((ML Notes))
    Content
      16 chapters
      214 pages
      Handwritten scans
    Topics covered
      Neural networks
      SVM
      Decision trees
      Reinforcement learning
    Format
      Chapter folders
      PDF download
    Audience
      ML students
      Chinese textbook readers

mindmap root((ML Notes)) Content 16 chapters 214 pages Handwritten scans Topics covered Neural networks SVM Decision trees Reinforcement learning Format Chapter folders PDF download Audience ML students Chinese textbook readers

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Things people build with this

USE CASE 1

Print and use as a study companion alongside Zhou Zhihua's machine learning textbook chapter by chapter

USE CASE 2

Review handwritten derivations for topics like support vector machines or neural networks

USE CASE 3

Download the full PDF to study ML fundamentals covered across all 16 chapters

Getting it running

Difficulty · easy Time to first run · 5min

PDF download requires accessing a Chinese cloud storage service linked via a WeChat public account.

Shared freely for personal study, no explicit license stated.

In plain English

This repository is a set of handwritten study notes for a well-known Chinese machine learning textbook called "Machine Learning" by Zhou Zhihua, sometimes called the "watermelon book" in Chinese academic circles. The notes were handwritten by Wang Bo (Kings), a PhD student in AI, and were scanned and organized for others to download and print. The full collection spans 16 chapters and 214 pages of A4 paper. The notes work through the textbook chapter by chapter, covering topics that form the foundation of modern machine learning: how to evaluate and choose between models, linear models, decision trees, neural networks, support vector machines, Bayesian classifiers, ensemble methods, clustering, dimensionality reduction, semi-supervised learning, probabilistic graphical models, rule learning, reinforcement learning, and the theoretical side of what makes a learning algorithm work. The repository stores the content as scanned images organized into folders by chapter. There is no runnable code and no software to install. It is purely a reference and study aid. A PDF version of all the notes is available for download via a Chinese cloud storage service, accessible through the associated WeChat public account mentioned in the README. The notes were last updated in March 2021, at which point all sixteen chapters were complete. Anyone studying machine learning from this particular textbook, especially those working through the mathematical derivations, may find these notes a useful companion. The project requires no technical setup to use.

Copy-paste prompts

Prompt 1

I'm studying Zhou Zhihua's machine learning textbook. Explain the difference between decision trees and Bayesian classifiers as covered in the book.

Prompt 2

Based on the topics in the watermelon book, help me understand how ensemble methods like boosting and bagging work and when to use each.

Prompt 3

I'm studying dimensionality reduction from Zhou Zhihua's textbook. Explain PCA and its relationship to eigenvalue decomposition in plain English.

Prompt 4

I'm working through the neural network chapter of the watermelon book. Explain backpropagation step by step without heavy math notation.

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