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 Statistical Learning
2.
授课院系
Originating Department
统计与数据科学系
3.
课程编号
Course Code
STA320
4.
课程学分 Credit Value
3
5.
课程类别
Course Type
专业核心课 Major Core Courses
6.
授课学期
Semester
秋季 Fall
7.
授课语言
Teaching Language
英文 English
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）
荆炳义，统计与数据科学系，jingby@sustech.edu.cn
9.
实验员/助教、所属学系、联系
方式
Tutor/TA(s), Contact
待公布 To be announced
10.
选课人数限额(可不填)
Maximum Enrolment
（Optional）
2
11.
授课方式
Delivery Method
讲授
Lectures
实验/实习
Lab/Practical
其它(请具体注明)
Other（Pleasespecify）
总学时
Total
学时数
Credit Hours
48
0
0
48
12.
先修课程、其它学习要求
Pre-requisites or Other
Academic Requirements
MA204, Mathematical Statistics 数理统计
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
Statistical Learning merges Statistics with Computer Science and Optimization. Much of the
agenda in Statistical Learning is driven by applied problems in science and technology, where data
streams are increasingly large-scale, dynamic, and heterogeneous, and where mathematical and
algorithmic creativity are required to bring statistical methodology to bear. The course covers a
wide range of topics, including supervised and unsupervised learning, kernel methods, model
selection, ensemble methods, graphical models. The goal is to study the underlying principles for
those methods and be able to tackle real-life problems.
16.
预达学习成果 Learning Outcomes
Statistical Learning is widely used in many areas. For instance, bioinformatics, artificial
intelligence, signal processing, communications, networking, information management, finance,
game theory and control theory are all being heavily influenced by developments in Statistical
Learning. Upon completion of the course, the students are expected to learn the essential
techniques and underlying principles behind Statistical Learning and be able to tackle real-life
problems using these tools.
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. Introduction.
2. Sparsity and Bias-Variance Trade-off
3. Linear Methods for Regression
4. Linear Methods for Classification
5. Basis Expansions and Regularization
6. Features and Kernel Methods.
7. Local Smoothing Methods
8. Model Selection
9. Support Vector Machine
10. Neural Networks
11. Tree-Based Methods
12. Boosting Techniques
13. Unsupervised Learning
14. Graphical Models
15. Model Averaging
16. Variational Inference
18.
教材及其它参考资料 Textbook and Supplementary Readings
1.
An Introduction
to
Statistical Learning. By James, G., Witten, D., Hastie, R., and Tibshirani, R.
2. The Elements of Statistical Learning. By Hastie, T., Tibshirani, R, Friedman, J.
3. Pattern Recognition and Machine Learning. By Bishop, C.M.
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
课堂表现
Class
Performance
小测验
Quiz
课程项目 Projects
20
平时作业
Assignments
20
期中考试
Mid-Term Test
期末考试
Final Exam
40
期末报告
20
4
Final
Presentation
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
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