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
大数据实证研究 Empirical Research in Big Data
2.
授课院系
Originating Department
信息系统与管理工程系 Department of Information Systems & Management Engineering
3.
课程编号
Course Code
MIS 402
4.
课程学分 Credit Value
3
5.
课程类别
Course Type
专业选修课 Major Elective Courses
6.
授课学期
Semester
春季 Spring
7.
授课语言
Teaching Language
中英双语 Bilingual
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）
卢涛，信息系统与管理工程系
Tao Lu, Department of Information Systems & Management Engineering
lut@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
32
32
0
64
12.
先修课程、其它学习要求
Pre-requisites or Other
Academic Requirements
MIS306 数据挖掘及商务应用
MIS306 Data Mining and Business Applications
13.
后续课程、其它学习规划
Courses for which this course
is a pre-requisite
无 None
14.
其它要求修读本课程的学系
Cross-listing Dept.
无 None
教学大纲及教学日历 SYLLABUS
15.
教学目标 Course Objectives
This course is designed to instruct the theoretical foundations as well as applications about deep learning algorithms for
high grade undergraduate students. From the theoretical perspective: the course will deliver: 1) fundamental machine
learning theories related to deep learning algorithms, and 2) underlying theoretical mechanism of deep learning models.
Also, from the application perspective: the course will guide the students to master: 1) How to use and apply deep
learning models, and 2) How to design and develop models in real life settings.
课程目标是教授本科高年级学生深度学习的基础理论和应用方法。理论基础方面，课程会教授一些深度学习模型会涉及到
的基础机器学习原理以及深度学习的理论原理。应用方面，课程会带领学生学会如何使用一些常用的深度学习模型，并且
指导学生在实际场景中应用深度学习技术。
16.
预达学习成果 Learning Outcomes
After learning the course, students are expected to:
1） Understand all the basic fundamental concepts in deep learning algorithms;
2） Mater the underlying theories and corresponding derivations;
3） Understand the underlying mechanisms of several commonly used model specifications;
4） Be able to use and apply these models into real life settings;
希望学生学完课程之后能够：
1） 理解所有深度学习算法的底层概念；
2） 掌握深度学习算法的理论和推导过程；
3） 理解一些常用深度学习模型的作用机制；
4） 能够在一些实际场景下使用和应用深度学习算法。
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.)