Statistical Machine Learning（研究生）
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
Class time&place: Mon:10.10-11.50 N202
Wed: 10.10-11.50 N202
Course Text:
1. Hastie, Tibshirani, and Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer.
2. James G, Witten D, Hastie T, et al. An introduction to statistical learning. New York: springer, 2013.
3. 方匡南. 数据科学.电子工业出版社.2018.6
Contents:
1. Introduction
2. Statistical Learning
3. Linear Regression
4. Classification
5. Resampling Methods
6. Linear Model Selection and Regularization
7. Moving Beyond Linearity
8. Decision Tree
9. Ensemble Learning
10. Support Vector Machines
11. Unsupervised Learning
12. Neural Network
Syllabus:
Slides:
ch13 Convolutional Neural Network.pdf
Reference:
SCAD: scad runze li.pdf
elastic-net: zou-elastic net.pdf
lasso: Regression-Shrinkage-and-Selection-via-the-Lasso.pdf
adaptive lasso: adaptive lasso.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
Two-part model: identification of porportionality structure with two-part model.pdf
integrative sparse PCA: iSPCA.pdf
Code:
Data:
电子版作业请发送到 dataminingxmu@163.com，邮件标题： quiz/Homwork 3+姓名+学号.
期末考 :
期末考主要是考核project，分为两部分，即presentation 和最终的project分析报告。
成绩：综合presentation和最终的project分析报告两部分成绩作为期末考成绩，再综合平时考勤、quiz、作业的成绩作为本门课的最终成绩。
说明：（1）每个小组1-2人 （2） project题目自选，可以做方法创新也可以做应用案例
1. Presentation:
（1）从第14周开始,每次课8组，每组presentation10分钟。
（2）以PPT或者latex slides形式汇报
（3）方法创新的需要报告 研究动机，文献综述，研究方法，模拟，（如有理论证明更好），应用案例，总结
（4）应用案例需要报告 研究动机（尤其是研究意义），文献综述，数据说明，不同方法的应用比较，总结
（5）presentation的顺序
2. Project报告：
（1）研究报告（可以是word也可以是latex的tex文档）
(2 ) code文档（为了可重复分析结果，最好用R做，也可以接受python等）
（3）数据和数据说明（说明数据的来源，商业意义，对应的变量含义）
（4）PPT
Project请在2020年6月17号24点之前发到dataminingxmu@163.com，如果数据文档太大了，可以用超大附件，或提供下载链接
