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
地球科学大数据与人工智能
Big Data and Artificial Intelligence in Geosciences
2.
授课院系
Originating Department
地球与空间科学系 Department of Earth and Space Sciences
3.
课程编号
Course Code
ESS412
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.
他授课教师)
Instructor(s), Affiliation&
Contact
For team teaching, please list
all instructors
陈克杰,地球与空间科学系
邮箱:chenkj@sustech.edu.cn
电话:0755-88018645
办公室:创园 9 310
Kejie Chen, Department of Earth and Space Sciences
E-mail: chenkj@sustech.edu.cn
Tel: 0755-88018645
Office: Innovation Park #9-310
9.
/助教系、
方式
Tutor/TA(s), Contact
待公布 To be announced
10.
选课人数限额(不填)
Maximum Enrolment
Optional
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)
OtherPlease specify
总学时
Total
11.
学时数
Credit Hours
12
48
2
12.
先修课程、其它学习要求
Pre-requisites or Other
Academic Requirements
MA107A 线性代数 ACS102B 计算机程序设计基础 B
MA107A Linear Algebra A and CS102B Introduction to Computer Programming B
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
本课程系统介绍地球科学大数据与人工智能的基本框架与原理,重点分析高维数据降维度、分类与预测、大图形社区
结构识别、无限流数据处理、机器学习及人工智能地学的建模过程。通过学习本课程,使学生具备运用地学大数据与机器
学习算法解决地球科学问题的基本能力。
This course systematically introduces the basic framework and principles of big data and artificial intelligence in earth
sciences, focusing on the analysis of high-dimensional data dimension descending, classification and prediction, large
graphic community structure identification, infinite stream data processing, machine learning and artificial intelligence
geoscience modeling. By studying this course, students will be able to use geoscience big data and machine learning
algorithms to solve earth science problems.
16.
预达学习成果 Learning Outcomes
学生完成本课程后,将会掌握以下知识:
1. 数据清洗与预处理;
2. 高维数据降维;
3. 分类与预测;
4. 图形数据处理;
5. 无限流数据与时间序列;
6. 机器学习与深度学习;
7. 贝叶斯原理与人工智能地震学。
Upon completing the course, students will master the following knowledge:
1. Data cleaning and pre-processing;
2. High-dimensional data dimensionality reduction;
3. Classification and prediction;
4. Graphical data processing;
5. Infinite stream data and time Series;
6. Machine learning and deep learning;
7. Bayesian principle and artificial intelligence seismology.
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
第一章:绪论 2 学时)
讲授 科学第四范式、地球科学大数据机器挖掘、建模
第二章:数据清洗与预处理(6 学时)
讲授 数据清洗的概念、数据集成与融合、数据变换、数据规约、离群点检测
第三章:高维数据降纬(6 学时)
讲授 相关分析、典型相关分析、哈希算法、主成分分析、因子分析
第四章:分类与预测(6 学时)
讲授 回归分析、聚类分析、判别分析、关联规则算法、推荐系统算法
第五章:无限流数据与时间序列6 学时)
讲授 无限流数据与时序模式、数据特征提取、时间序列算法
第六章:机器学习与深度学习(10 学时)
讲授 机器学习的发展史、机器学习分类、人工神经网络、深度学习
第七章:应用实践:利用机器学习区分地震信号与背景噪声(12 学时)
实验 利用 Python TensorFlowKeras 等进行地震观测值样本训练,评价不同分类方法表现
Chapter 1: Introduction (2 hours)
The fourth normal form of science, big data machine mining and modeling of earth sciences
Chapter 2: Data Cleaning and Preprocessing (6 hours)
The concepts of data cleaning, data integration and fusion, data transformation, data protocol, outlier detection
Chapter 3: Dimension Reduction in High-Dimensional Data (6 hours)
Correlation analysis, canonical correlation analysis, hash algorithm, principal component analysis, factor analysis
Chapter 4: Classification and Forecasting (6 hours)
Regression analysis, cluster analysis, discriminant analysis, association rule algorithm, recommendation system
algorithm
Chapter 5: Infinite Stream Data and Time Series (6 hours)
Infinite stream data and time series patterns, data feature extraction, time series algorithms
Chapter 6: Machine Learning and Deep Learning (10 hours)
The history of machine learning, classification of machine science, artificial neural networks, and deep learning
Chapter 7: Practise: Seismic signal/noise discrimination with machine learning (12 hours)
Using Python modules such as TensorFlow, Keras to apply machine learning to train dasets, assessing the performace
of different classifiers
18.
教材及其它参考资料 Textbook and Supplementary Readings
教材:
周永章,张良均,张奥多,王俊。地球科学大数据挖掘与机器学习,中山大学出版社,2018
参考资料:
1. Trevor Hastie, Robert Tibshirani, Jerome Friedman. The Elements of Statistical Learning, Springer, 2017
2. Ian GoodfellowYoshua BengioAaron Courville. Deep Learning, MIT Press, 2016
3. 周志华。机器学习,清华大学出版社,2016
课程评估 ASSESSMENT
4
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
10
课堂表现
Class
Performance
小测验
Quiz
课程项目 Projects
平时作业
Assignments
20
期中考试
Mid-Term Test
30
期末考试
Final Exam
40
期末报告
Final
Presentation
其它(可根据需
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/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/员会审议通过
This Course has been approved by the following person or committee of authority
地球与空间科学系本科教学指导委员会