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
大数据导论 Introduction to Big Data Science
2.
授课院系
Originating Department
数学 Mathematics
3.
课程编号
Course Code
MA333
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
秋季 Fall
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）
张振，数学系，副教授
慧园 3 栋 417
zhangz@sustc.edu.cn
Zhang Zhen, Mathematics, Associate Professor
Room 417, Block 3, Wisdom Valley
zhangz@sustc.edu.cn
9.
实验员/助教、所属学系、联系
方式
Tutor/TA(s), Contact
无 NA
10.
选课人数限额(可不填)
Maximum Enrolment
（Optional）
50
授课方式
Delivery Method
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
48
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
数据挖掘 Data Mining，统计机器学习 Statistical Machine Learning，大数据计算 Big Data
Computing
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
1． 介绍大数据科学的基本概念和研究对象 Show the basic concepts and objectives of big data research
2． 传授大数据科学的基本方法论以及数学模型 Teach basic methodology of big data science, including mathematical modeling
3． 引导学生用 Python 语言编程处理数据，解决实际问题 Guide students to programming and data processing with R and
solving real problems
16.
预达学习成果 Learning Outcomes
通过本课程学习，学生将能够：
By the end of the semester, the students will be able to:
1． 描述现实生活中的大数据问题 Describe the big data problems in real life
2． 将大数据问题转化为数学和可计算模型 Turn the big data problems into mathematical and computational models
3. 掌握大数据科学的基本方法论,如分类模型、回归模型、聚类模型、模型选择和降维等方法 Master the basic methodology
of big data science, e.g., Classification, regression, clustering, model selection, dimension reduction, etc.
4．了解热门应用问题的算法机理，如自然语言处理、文本分析、社交网络分析、神经网络和深度学习、分布式计算，推
荐系统和在线学习等 Get to know hot topics in applications, e.g., Natural language processing (NLP), text analysis, social network
analysis, neural network and deep learning, distributed computing, recommender systems, online learning, etc.
5．学会用 Python 语言编程以及对实际数据的处理，包括数据收集、提取、集成和清洁，以及数据挖掘 Learn programming
and processing real data with python, including data collection, data extraction, data integration , data cleansing, and data 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.)
第一章：大数据简介：数据挖掘和机器学习（2 学时）
Chapter 1: Introduction to big data science: Data mining and machine learning (2 hours);
3
第二章：数据预处理：数据收集、提取和清理，python 语言程序简介（4 学时）
Chapter 2: Data preprocessing: Data collection, data extraction, and data cleansing, python programming (4 hours)
第三章：回归模型：线性回归和正则化方法（4 学时）
Chapter 3: Supervised learning: Linear regression, regularization methods: Ridge and Lasso (4 hours)
第四章：分类模型：k-近邻，决策树，支持向量机，逻辑回归和朴素贝耶斯法则（8 学时）
Chapter 4: Classification: k-nearest neighbours, decision trees, support vector machine, logistic regression and Naïve Bayes rules (8
hours)
第五章：集成算法：袋装，随机森林，提升，AdaBoost 算法，梯度提升决策树（4 学时）
Chapter 5: Ensemble learning: Bagging, Stochastic forests, boosting, AdaBoost, gradient boosting decision tree (GBDT) (4 hours)
第六章：聚类模型：k 平均方法，层次聚类（4 学时）
Chapter 6: Clustering models: k-means, hierarchical clustering, association rules (4 hours)
第七章：特征与模型选择：偏差-方差分解，评价指标，交叉核实（2 学时）
Chapter 7: Feature and model selection: Bias-variance decomposition, evaluation indices, cross-validation (2 hours)
第八章：降维：线性判别分析，主成分分析（4 学时）
Chapter 8: Dimension reduction: Linear discriminant analysis (LDA), principle component analysis (PCA) (4 hours)
第九章：EM 算法和高斯混合模型（2 学时）
Chapter 9: Expectation-Maximization (EM) methods and Gaussian mixed models (2 hours)
第十章：概率图模型，图算法与社交网络分析（4 学时）
Chapter 10: Probabilistic graphical models, graphical algorithms and social network analysis (4 hours)
第十一章：神经网络与深度学习（4 学时）
Chapter 11: Neural networks and deep learning (4 hours)
第十二章：自然语言处理和文本分析（4 学时）
Chapter 12: Natural language processing (NLP) and text analysis (4 hours)
第十三章：推荐系统（2 学时）
Chapter 13: Recommender systems (2 hours)
第十四章：其他专题：在线学习，大规模数据与分布式计算（如有时间）
Chapter 14: Online learning, large scale data and distributed computing (if time permit)
18.
教材及其它参考资料 Textbook and Supplementary Readings
4
参考教材 Textbook：
1. 数据科学导引，欧高炎等著，高等教育出版社，2017.
2. 机器学习，周志华 著，清华大学出版社，2016.
3. An Introduction to Statistical Learning with Applications in R, by Gareth James, Daniela Witten, Trevor Hastie and Robert
Tibshirani, Springer, 2013.
4. Machine Learning, by Tom Mitchell, McGraw Hill, 1997.
5. Pattern Recognition and Machine Learning, by Christopher M. Bishop, Springer, 2006.
其他参考资料 Supplementary Readings：
The Elements of Statistical Machine Learning: Data mining, Inference and Prediction, 2nd Edition, by Trevor Hastie, Robert
Tibshirani, and Jerome Friedman, Springer, 2009.
Pattern Classification, 2nd Edition, by Richard O. Duda, Peter E. Hart, and David G. Stork, John Wiley & Sons, Inc., 2000.
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
10%
课程项目
Projects
平时作业
Assignments
30%
期中考试
Mid-Term Test
30%
期末考试
Final Exam
30%
期末报告
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
B. 二级记分制（通过/不通过） Pass/Fail Grading
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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