Course description:This course provides an accessible overview of the field of data mining and statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This course presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this course is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform.
Prerequisites: Probability and Mathematical Statistics, R programming skill
James G, Witten D, Hastie T, et al. An introduction to statistical learning. New York: springer, 2013.
Hastie, Tibshirani, and Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer.
方匡南 朱建平 姜叶飞. R数据分析. 电子工业出版社.2015. 2
方匡南. 数据科学. 电子工业出版社. 2018
1. Introduction ch1 introduction.pdf
2. Statistical Learning. ch2 statistical_learning.pdf
3. Linear Regression ch3 linear regression.pdf
4. Classification ch4 linear classification.pdf
5. Resampling Methods ch5 cv_boot.pdf
6. Linear Model Selection and Regularization ch6 model_selection.pdf
7. Moving Beyond Linearity ch7 nonlinearity .pdf
8. Tree-Based Methods ch8 trees.pdf
9. Support Vector Machines ch9 SVM .pdf
10. Unsupervised Learning ch10 unsupervised.pdf
Quiz : 发到邮箱datamining_under@163.com, 邮件标题：quiz/Homwork#+姓名+学号