Data Management Systems
This GitBook notes are maintained by zealscott.
Material
Syllabus
| Time | Slides | CS229 | Notes | Reading Material |
|---|---|---|---|---|
| 2.24 | 统计学习概述 | The Motivation & Applications of Machine Learning | video1 | |
| 2.27 | 感知机/KNN | An Application of Supervised Learning | notes1/video2 | |
| 3.6 | 朴素贝叶斯法/Logistic回归 | LR Newton’s method Perceptron |
线性回归与分类 | Ps0 notes2/video3 |
| 3.13 | 最大熵模型/SVM | Gerentive model, naive Bayes | 生成模型 | video4/5 |
| 3.19 | SVM | SVM/Dual/SMO | SVM理解 | video6/7/8 |
| 3.27 | Adaboost/GBDT | GBDT&XGboost小记 | ||
| 4.3 | EM算法 | Bias/Variance/ERM | Learning Theory | notes4/video9 |
| 4.10 | HMM | Regularization | Regularization&feature selection | notes5/video10 |
| 4.17 | CRF | EM 算法 Online learning Kmeans |
Online Learning Advice for applying ML EM 思想 |
notes6/7 video11/12 Advice for applying ML |
| 4.24 | CRF(2) | EM算法推导 | notes8 | |
| 5.8 | NeuralNetwork | Factor Analysis EM算法 |
Factor Analysis | notes9 Video13/14 |
| 5.15 | ||||
| 5.22 | PCA | PCA | PCA算法推导 | notes10 Video15 |
| 5.29 | Feature selection | |||
| 6.6-6.20 | ML theory |