14
2016
02

【课程】Statistical Learning(研究生)

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.     方匡南 兰伟.数据挖掘与机器学习.高等教育出版社.2024

3.    方匡南. 数据科学.电子工业出版社.2018.6


Contents:

1.      Introduction 

2.      Decision Tree

3.      Ensemble learning

4.      Support Vector Machines 

5.      Neural Network

6.      CNN

7.      RNN

8.      Survival Analysis

9.      Clustering Analysis 

10.   Recommendation System

11.   Network Analysis

12.   Project and Presentation




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:

credit.csv.zip

advertising.csv.zip

BASKETS1n.zip


 

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










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