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
人工智能（H） Artificial Intelligence (H)
2.
授课院系
Originating Department
计算机科学与工程系 Department of Computer Science and Engineering
3.
课程编号
Course Code
CS311
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
夏季 Summer
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）
袁博，助理教授，计算机科学与工程系，yuanb@sustech.edu.cn
Bo Yuan, Assistant Professor, Department of Computer Science and Engineering,
yuanb@sustech.edu.cn
9.
实验员/助教、所属学系、联系
方式
Tutor/TA(s), Contact
赵耀，教学实验师，计算机科学与工程系 ，zhaoy6@sustech.edu.cn
Yao Zhao, Teaching Technician, Department of Computer Science and Engineering,
zhaoy6@sustech.edu.cn
10.
选课人数限额(可不填)
Maximum Enrolment
（Optional）
2
11.
讲授
Lectures
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
32
32
64
12.
先修课程、其它学习要求
Pre-requisites or Other
Academic Requirements
CS102A 计算机程序设计基础 A Introduction to Computer Programming A
CS203 数据结构与算法分析 Data Structures and Algorithm Analysis
MA212 概率论与数理统计 Probability and Statistics
Comfortable programming in language such as C (or C++) Java or Python, some
knowledge of algorithmic concepts such as running times of algorithms; having some
rough idea of what NP-hard mean some familiarity with probability (we will go over this
from the beginning but we will cover the basics only briefly) not scared of mathematics,
some background in discrete mathematics, able to do simple mathematical proofs.
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
本课程将介绍人工智能比较重要的若干专题，包括搜索、博弈、约束满足、逻辑、机器学习和自然语言理解。学生在课程
中需要完成两至三个编程项目，编程语言可以是 Java 或 Python 或其他，并确保自己的程序可以正确运行。本课程的目的
是希望通过理论学习和实验，使得同学们可以掌握人工智能常用模型和算法，并熟练运用到实际问题中，也为后续学习更
高级的课程和相关研究打下基础。
Artificial Intelligence (AI) is a big field, and this course is an introduction to AI for undergraduate students. We will try to
explore the most important topics of the field, which encompasses search, game, constraint satisfaction problem (CSP),
logic, machine learning, and natural language processing (NLP), and we will go into some depth. We will have 2 or 3
mini-projects in this course, and the programing language can be Java or Python. The students’ programs will be
partially automatically graded, so they must be written to run on the computers. The goal is to provide every student who
takes the course a basic set of ideas and tools to employ on AI, and to be able to pursue advanced study and research
in the field if desired.
16.
预达学习成果 Learning Outcomes
同学们可以掌握人工智能常用模型和算法，包括搜索、博弈、约束满足、逻辑、机器学习和自然语言理解；并熟练运用到
实际问题中，也为后续学习更高级的课程和相关研究打下基础。
1. Students will demonstrate an understanding of the idea of AI and agent-based AI.
2. Students will demonstrate an understanding of basic and advanced search-based agent, game, and constraint
satisfaction problem (CSP).
3. Students will demonstrate an understanding of basic logic-based agents, e.g., propositional logic, first order logic.
4. Students will demonstrate an understanding of the most widely-used machine learning algorithms, and basic NLP
method.
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
理论课 （32 学时） LECTURE (32 Credit Hours):
Lec1 介绍 Introduction
Lec2 智能体 Intelligent agent
Lec3 无信息搜索 Uninformed Search
Lec4 有信息搜索 Informed Search
Lec5 局部搜索 Local Search
Lec6 对抗搜索（博弈） Adversarial Search (Game)
Lec7 约束满足问题 Constraint satisfaction problem
Lec8 命题逻辑 Propositional logic
Lec9 一阶逻辑 First order logic
Lec10 机器学习概念 Machine Learning Concepts
Lec11 线性回归和逻辑回归 Linear Regression & Logistic Regression
Lec12 感知器和神经网络 Perceptron & Neural Networks
Lec13 决策树和朴素贝叶斯 Decision tree & Naive Bayes
Lec14 集成学习和聚类 Ensemble learning & Clustering
Lec15 自然语言处理 Natural language processing
Lec16 总结 Summary and Review
实验课（32 学时） LAB (32 Credit Hours):
Lab1 Python 介绍 Introduction to Python I
Lab2 Python 介绍 Introduction to Python II
Lab3 无信息搜索及实现 Uninformed Search and Implementation
Lab4 有信息搜索其实现 Informed Search and Implementation
Lab5 局部搜索及实现 Local Search and Implementation
Lab6 对抗搜索（博弈）及实现 Adversarial Search (Game) and Implementation
Lab7 约束满足问题及其实现 Constraint satisfaction probem and Implementation
Lab8 命题逻辑及实现 Propositional logic and Implementation
Lab9 一阶逻辑及实现 First order logic and Implementation
Lab10 机器学习基础及实现 Machine Learning Concepts and Implementation
Lab11 线性回归和逻辑回归及实现 Linear Regression & Logistic Regression and Implementation
Lab12 感知器和神经网络及实现 Perceptron & Neural Networks and Implementation
Lab13 决策树和朴素贝叶斯及实现 Decision tree & Naive Bayes and Implementation
Lab14 集成学习和聚类及实现 Ensemble learning & Clustering and Implementation
Lab15 自然语言处理及实现 Natural language processing and Implementation
Lab16 总结 Summary and Review
4
18.
教材及其它参考资料 Textbook and Supplementary Readings
Textbook: Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach (Third edition), Cambridge
University Press, 2009.
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
10%
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
50%
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
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