1
课程详述
COURSE SPECIFICATION
以下课程信息可能根据实际授课需要或在课程检讨之后产生变动。如对课程有任何疑问，请联
系授课教师。
The course information as follows may be subject to change, either during the session because of unforeseen
circumstances, or following review of the course at the end of the session. Queries about the course should be
directed to the course instructor.
1.
课程名称 Course Title
统计学习的基本原理 (Introduction to Statistical Learning)
2.
授课院系
Originating Department
数学系 (Mathematics)
3.
课程编号
Course Code
4.
课程学分 Credit Value
32
5.
课程类别
Course Type
专业选修课 Major Elective Courses
6.
授课学期
Semester
夏季 Summer
7.
授课语言
Teaching Language
中英双语 English & Chinese
8.
授课教师、所属学系、联系方
式（如属团队授课，请列明其
他授课教师）
Instructor(s), Affiliation&
Contact
（For team teaching, please list
all instructors）
彭衡 (PENG Heng)，香港浸会大学数学系 (Department of Mathematics, Hong
Kong Baptist University)
9.
实验员/助教、所属学系、联系
方式
Tutor/TA(s), Contact
无 NA（请保留相应选项 Please only keep the relevant information）
10.
选课人数限额(可不填)
Maximum Enrolment
（Optional）
授课方式
Delivery Method
讲授
Lectures
习题/辅导/讨论
Tutorials
实验/实习
Lab/Practical
其它(请具体注明)
Other（Please specify）
总学时
Total
11.
学时数
Credit Hours
32
32
2
12.
先修课程、其它学习要求
Pre-requisites or Other
Academic Requirements
概率论与数理统计 或者 概率论
Probability and Statistics or Probability Theory
13.
后续课程、其它学习规划
Courses for which this course
is a pre-requisite
14.
其它要求修读本课程的学系
Cross-listing Dept.
教学大纲及教学日历 SYLLABUS
15.
教学目标 Course Objectives
统计学习是指的基于统计原理针对复杂数据建模，分析解释和预测的一类统计方法。 这些方法混合和伴随着计算机
科学，特别是机器学习的发展。随着大数据时代的到来，统计学习的方法和理论正被推广和应用到科学研究，商务市场分
析，以及金融行业等各行业进行数据分析和信息挖掘。 本次课程的主要目的是介绍统计学习方法的基本统计原理和思想，
以及概述几种常见的统计学习方法， 包括稀疏回归，分类，随机森林，boosting 和向量机等具体实现及应用。
Statistical learning refers to a class of statistical methods based on statistical principles for complex data
modeling, analytical interpretation and prediction. These methods are mixed and accompanied by the
development of computer science, especially machine learning. With the arrival of large data age, statistical
learning methods and theories are being promoted and applied to scientific research, business market
analysis, and financial industry and other industries for data analysis and information mining. The main
purpose of this course is to introduce the basic statistical principles and ideas of statistical learning methods,
and to outline several common statistical learning methods, including sparse regression, classification,
random forest, boosting and vector machine, and their realization and applications.
16.
预达学习成果 Learning Outcomes
通过课程的学习，学生可以掌握统计学习方法的基本原理，能够应用统计软件 比如 R，实现基本的统计学习算法，进
行有效的数据分析和信息挖掘。
Through the course study, students can master the basic principles of statistical learning methods, can
apply statistical software such as R, implement basic statistical learning algorithms, and conduct effective
data analysis and information mining.
17.
课程内容及教学日历 （如授课语言以英文为主，则课程内容介绍可以用英文；如团队教学或模块教学，教学日历须注明主
讲人）
Course Contents (in Parts/Chapters/Sections/Weeks. Please notify name of instructor for course section(s), if
this is a team teaching or module course.)
3
1、统计学习的基本介绍（2 学时），什么是统计学习。
实验课（2 学时），介绍统计学习软件 R 及其编程语言。
2、回归模型在统计学习的应用（2 学时）。
实验课（2 学时），讲解如何使用统计学习软件 R 基于回归分析进行统计学习。
3、分类判别算法在统计学习的应用（3 学时）。
4、再抽样方法在统计学习中的应用（2 学时）。
实验课（2 学时），讲解如何使用统计学习软件 R 对数据进行分类判别， 以及如何进行在抽样方法进行推断。
5、正则方法在线性回归模型中的应用（2 学时）。
实验课（2 学时）， 讲解如何使用统计学习软件 R 如何实现正则方法的应用。
6、非参数回归方法（3 学时）。
7、基于回归树的统计学习方法（2 学时）。
实验课（2 学时），讲解如何使用统计学习软件 R 如何实现非参数回归方法的应用，基本基于回归树的 R 软件包的应用。
8、支持向量机的简单介绍（2 学时）。
支持向量机在统计学习软件 R 中的实现及应用（2 学时）。
9、非监督统计学习方法的介绍（3 学时）。
1.Basic introductions to statistical learning (2 Credit Hours), and what is the statistical study.
Lab lessons (2 Credit Hours), introduction of statistical learning software R and its programming language.
2.The application of regression models in statistical learning (2 Credit Hours).
Lab lessons (2 Credit Hours), explain how to use statistical learning software R to perform statistical learning based on regression
analysis.
3.The application of classification and discriminant algorithms in statistical learning (3 Credit Hours).
4.The application of resampling methods in statistical learning (2 Credit Hours).
Lab lessons (2 Credit Hours), explain how to use the statistical learning software R to classify and discriminate data, and how to
make inferences by the resampling methods.
5.The application of the regular methods in the linear regression model (2 Credit Hours).
Lab classes (2 Credit Hours), explain how to use the statistical learning software R to achieve the application of the regular methods.
6.Nonparametric regression method (3 Credit Hours).
7.The statistical learning methods based on tree approaches (2 Credit Hours).
Lab classes (2 Credit Hours), explain how to use the statistical learning software R to achieve the application of nonparametric
regression methods, and the application of the regression tree methods by R packages.
8.A brief introduction of the support vector machine (2 Credit Hours).
Lab classes (2 Credit Hours), the implementation and application of support vector machine in statistical learning by software R.
9.Introduction the unsupervised statistical learning methods (3 Credit Hours).
18.
教材及其它参考资料 Textbook and Supplementary Readings
Text: An Introduction to Statistical Learning (统计学习导论), Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani
课程评估 ASSESSMENT
19.
评估形式
Type of
Assessment
评估时间
Time
占考试总成绩百分比
% of final
score
违纪处罚
Penalty
备注
Notes
出勤 Attendance
4
课堂表现
Class
Performance
小测验
Quiz
课程项目 Projects
平时作业
Assignments
40
期中考试
Mid-Term Test
期末考试
Final Exam
期末报告
Final Presentation
60
其它（可根据需要
改写以上评估方
式）
Others (The
above may be
modified as
necessary)
20.
记分方式 GRADING SYSTEM
A. 十三级等级制 Letter Grading
B. 二级记分制（通过/不通过） Pass/Fail Grading
课程审批 REVIEW AND APPROVAL
21.
本课程设置已经过以下责任人/委员会审议通过
This Course has been approved by the following person or committee of authority
