## 【课程】Data Mining and Machine Learning（本科）

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

Reference Book:

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.

Contents:

1.          Introduction ch1 Introduction1.pdf  ch1 introduction2.pdf

2.     Statistical Learning. ch2 statistical_learning.pdf

3.          Linear Regression  ch3 linear regression.pdf

4.          Classification  ch4 linear classification.pdf

5.          Resampling Methods

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

11. Deep Learning Ch11_Deep_Learning.pdf

Data: data.zip

elastic-net: zou-elastic net.pdf

group lasso:group lasso.pdf

structured sparse logistic regression: Structured sparse logistic regression with application to lung cancer prediction using breath volatile biomarkers.pdf

integrative sparse PCA: iSPCA.pdf