DB004111-统计学习理论(全英文)

发布者:王源发布时间:2021-05-29浏览次数:337

研究生课程开设申请表

开课院(系、所):    十大澳门网站赌博   

课程申请开设类型: 新开     重开□     更名□请在内打勾,下同

课程

名称

中文

统计学习理论

英文

Statistical Learning Theory

待分配课程编号

DB004111

课程适用学位级别

博士

硕士


总学时

32

课内学时

32

学分

2

实践环节

0

用机小时

0

课程类别

公共基础     专业基础     专业必修     专业选修

开课院()

十大澳门网站赌博   

开课学期

春季

考核方式

A.笔试(开卷   闭卷)      B. 口试    

C.笔试与口试结合                 D. □其他

课程负责人

教师

姓名

康维

职称

教授

e-mail

wkang@seu.edu.cn

网页地址


授课语言

英语

课件地址


适用学科范围

信息,数学,计算机等

所属一级学科名称

信息与通信工程

实验(案例)个数


先修课程


教学用书

教材名称

教材编者

出版社

出版年月

版次

主要教材

Foundations of Machine Learning

Mehryar Mohri

MIT Press

2018

2

主要参考书

Undearstanding Machine Learning

Shai Shalev-Shwartz

Cambridge University Press

2014

1












一、课程介绍(含教学目标、教学要求等)300字以内)

本课程涵盖机器学习领域的基础理论方面,包含监督学习的PAC模型,PAC可学性,过拟合,统一收敛性,奥卡姆剃刀,VC维数,Rademacher复杂度,性能增强,统计询问方法和二进制傅立叶方法,隐私保护或通信约束下的学习算法复杂度等。希望通过本课程,同学可以对于机器学习算法的可学性和复杂度理论得到初步的认识,对于后续机器学习方面的科研起到帮助的作用。


二、教学大纲(含章节目录):(可附页)

1. PAC模型和PAC 可学性

2. 过拟合问题和奥卡姆剃刀

3. 统一收敛性

4. VC理论和无限维问题的可学性

5. Radamacher 复杂度

6. 性能增强

7. 统计询问和傅立叶方法

8. 隐私保护或通信约束下的学习算法


三、教学周历

周次

教学内容

教学方式

 1

PAC模型和PAC 可学性

讲课

 2

PAC模型和PAC 可学性

讲课

 3

过拟合问题和奥卡姆剃刀

讲课

 4

统一收敛性

讲课

 5

统一收敛性

讲课

 6

VC理论和无限维问题的可学性

讲课

 7

VC理论和无限维问题的可学性

讲课

 8

VC理论和无限维问题的可学性

讨论

 9

Radamacher 复杂度

讲课

 10

Radamacher 复杂度

讲课

 11

性能增强

讲课

 12

统计询问和傅立叶方法

讲课

 13

统计询问和傅立叶方法

讲课

 14

统计询问和傅立叶方法

讨论

 15

隐私保护或通信约束下的学习算法

讲课

 16

隐私保护或通信约束下的学习算法

讨论

 17



 18



注:1.以上一、二、三项内容将作为中文教学大纲,在研究生院中文网页上公布,四、五内容将保存在研究生院。2.开课学期为:春季、秋季或春秋季。3.授课语言为:汉语、英语或双语教学。4.适用学科范围为:公共,一级,二级,三级。5.实践环节为:实验、调研、研究报告等。6.教学方式为:讲课、讨论、实验等。7.学位课程考试必须是笔试。8.课件地址指在网络上已经有的课程课件地址。9.主讲教师简介主要为基本信息(出生年月、性别、学历学位、专业职称等)、研究方向、教学与科研成果,以100500字为宜。


四、主讲教师简介:

康维,19798月生,男,博士学历,十大澳门网站赌博,信息与信号处理系教授。研究方向为信息论及其应用,信息安全和隐私保护,和统计学习理论。教学工作目前承担本科生课程《数据安全与隐私保护》,博士生课程《网络信息论》。科研方面多年来连续主持国家自然科学基金项目,发表高等级论文多篇。




五、任课教师信息(包括主讲教师):

任课

教师

学科

(专业)

办公

电话

住宅

电话

手机

电子邮件

通讯地址

邮政

编码

康维

信息与信号处理



 

wkang@seu.edu.cn





















六、课程开设审批意见

所在院(系)



负责人:

期:

所在学位评定分

委员会审批意见



分委员会主席:

期:

研究生院审批意见




负责人:

期:


说明:1.研究生课程重开、更名申请也采用此表。表格下载:http:/seugs.seu.edu.cn/down/1.asp

2.此表一式三份,交研究生院、院(系)和自留各一份,同时提交电子文档交研究生院。









Application Form For Opening Graduate Courses

School (Department/Institute)School of Information Science and Engineering

Course Type: New Open    Reopen □   Rename □Please tick in □, the same below

Course Name

Chinese

统计学习理论

English

Statistical Learning Theory

Course Number

DB004111

Type of Degree  

Ph. D

Master


Total Credit Hours

32

In Class Credit Hours

32

Credit

2  

Practice


Computer-using Hours


Course Type

□Public Fundamental    □Major Fundamental    □Major CompulsoryMajor Elective

School (Department)

School of Information Science and Engineering

Term

Spring

Examination

A. □PaperOpen-book   □ Closed-bookB. □Oral    

C.Paper-oral Combination                       D. □ Others

Chief

Lecturer

Name

Wei Kang

Professional Title

Professor

E-mail

wkang@seu.edu.cn

Website


Teaching Language used in Course

English

Teaching Material Website


 Applicable Range of Discipline

Information Science, Mathematics, Computer Science

Name of First-Class Discipline

Information and Communication Engineering

Number of Experiment


Preliminary Courses


Teaching Books

Textbook Title

Author

Publisher

Year of Publication

Edition Number

Main Textbook

Foundations of Machine Learning

Mehryar Mohri

MIT Press

2018

2

Main Reference Books

Undearstanding Machine Learning

Shai Shalev-Shwartz

Cambridge University Press

2014

1












  1. Course Introduction (including teaching goals and requirements) within 300 words:

We cover the basic theories of the area of machine learning, including PAC model for supervised learning, PAC learnability, overfitting, uniform convergence, Ocam’s razor, VC dimension, Rademacher complexity, boosting, statistical querya and binary fourier methods, learning complexity under privacy or communication constraints. Through this course, the students hopefully can obtain the basic understanding of the theories of the learnability and the complexity of machine learning and prepare for the future researches in the area of machine learning.


  1. Teaching Syllabus (including the content of chapters and sections. A sheet can be attached):

1. PAC model and PAC learnability

2. Overfitting and Occam’s razor

3. Uniform convergence

4. VC theory and learnabitliy of infinite hypothesis space

5. Radamacher complexity

6. Boosting

7. Statistical query and fourier methods

8. Learning under privacy or communication constraints.




  1. Teaching Schedule:


 Week

 Course Content

 Teaching Method

 1

PAC model and PAC learnability

 lecture

 2

PAC model and PAC learnability

 lecture

 3

Overfitting and Occam’s razor

 lecture

 4

Uniform convergence

 lecture

 5

Uniform convergence

 lecture

 6

VC theory and learnabitliy of infinite hypothesis space

 lecture

 7

VC theory and learnabitliy of infinite hypothesis space

 lecture

 8

VC theory and learnabitliy of infinite hypothesis space

 seminar

 9

Radamacher complexity

 lecture

 10

Radamacher complexity

 lecture

 11

Boosting

 lecture

 12

Statistical query and fourier methods

 lecture

 13

Statistical query and fourier methods

 lecture

 14

Statistical query and fourier methods

 seminar

 15

Learning under privacy or communication constraints

 lecture

 16

Learning under privacy or communication constraints

 seminar

 17



 18



Note: 1.Above one, two, and three items are used as teaching Syllabus in Chinese and announced on the Chinese website of Graduate School. The four and five items are preserved in Graduate School.


 2. Course terms: Spring, Autumn , and Spring-Autumn term.   

 3. The teaching languages for courses: Chinese, English or Chinese-English.  

 4. Applicable range of discipline: public, first-class discipline, second-class discipline, and third-class discipline.  

 5. Practice includes: experiment, investigation, research report, etc.  

 6. Teaching methods: lecture, seminar, practice, etc.  

 7. Examination for degree courses must be in paper.  

 8. Teaching material websites are those which have already been announced.  

 9. Brief introduction of chief lecturer should include: personal information (date of birth, gender, degree achieved, professional title), research direction, teaching and research achievements. (within 100-500 words)  


  1. Brief Introduction of Chief lecturer:

 Wei Kang, born in Aug. 1979, male,  PhD., Professor in Department of information and signal processing, School of information science and Engineering. Research areas include information theory and its applications, information security and privacy protection, and statistical learning theory. Current teaching includes undergraduate course <Data security and privacy protection> and Phd course <Network information theory>. Prof. Kang is the PI for multiple projects for Natural science foundation of China and has published multiple high-level journal papers.  




  1. Lecturer Information (include chief lecturer)


Lecturer

 Discipline

 (major)

 Office

Phone Number

Home Phone Number

Mobile Phone Number

 Email

Address

Postcode

 Wei Kang

 Informaiton and Signal Processing



 

 wkang@seu.edu.cn






















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