This article is mainly used to record the study notes for studying "Machine Learning" (Watermelon Book) by Zhou Zhihua. It is mainly based on the content of the textbook. The preliminary plan is to slowly organize the knowledge points according to the chapter list. I hope to learn from each other and improve together!
Free PDF download: "Machine Learning" (Watermelon Book)
table of Contents
Watermelon Book Reading Notes (1)-Introduction
Watermelon book reading notes (2)-model evaluation and selection
Watermelon book reading notes (3)-linear model
Watermelon Book Reading Notes (4)-Decision Tree
Watermelon book reading notes (5)-neural network
Watermelon book reading notes (6)-support vector machine
Watermelon book reading notes (7)-Bayesian classifier
Watermelon book reading notes (eight)-integrated learning
Watermelon book reading notes (9)-clustering
Watermelon book reading notes (10)-dimensionality reduction and metric learning (to be updated)
Watermelon book reading notes (11)-feature selection and sparse learning (to be updated)
Watermelon Book Reading Notes (12)-Computational Learning Theory (to be updated)
Watermelon book reading notes (13)-semi-supervised learning (to be updated)
Watermelon Book Reading Notes (14)-Probability Graphic Model (to be updated)
Watermelon book reading notes (15)-rule learning (to be updated)
Watermelon Book Reading Notes (16)-Intensive Learning (to be updated)
Watermelon Book Reading Notes (17)-Questions related to the appendix (to be updated)