课程大纲
COURSE SYLLABUS
1.
课程代码/名称
Course Code/Title
CSE5001／高级人工智能 Advanced Artificial Intelligence
2.
课程性质
Compulsory/Elective
Compulsory
3.
课程学分/学时
Course Credit/Hours
3/64
4.
授课语言
Teaching Language
中文/ Chinese
5.
授课教师
Instructor(s)
张宇/Yu Zhang
6.
是否面向本科生开放
Open to undergraduates
or not
是/Yes
7.
先修要求
Pre-requisites
（ 如 面 向 本 科 生 开 放 ， 请 注 明 区 分 内 容 。 If the course is open to
undergraduates, please indicate the difference.）
CS303／人工智能 Artificial Intelligence
8.
教学目标
Course Objectives
This course introduces recent advances in artificial intelligence. Topics covered include intelligent
optimization and learning, as well as case studies in machine learning. The assessment in the course will
consist of assignments, a mid-term test (or project), and a final exam.
Upon finishing this course, students are expected to have a good understanding of challenging
optimization and learning problems in AI, and different models and algorithms for tackling these
problems.
9.
教学方法
Teaching Methods
Lectures and tutorials
10.
教学内容
Course Contents
Section 1
Introduction
Section 2
Basic Search
Section 3
Heuristic Search
Section 4
Meta Heuristics
Section 5
Supervised Learning
Section 6
Ensemble Learning
Section 7
Multi-objective Learning
Section 8
Unsupervised Learning
Section 9
Feature Engineering
Section 10
Markov Decision Process
Section 11
Reinforcement Learning
Section 12
Natural Language Processing
11.
课程考核
Course Assessment
（
○
1
考核形式 Form of examination；
○
2
.分数构成 grading policy；
○
3
如面向本科生开放，请注明区分内容。
If the course is open to undergraduates, please indicate the difference.）
Assignments: 40%
Mid-term test/Project: 20%
Final Exam: 40%
12.
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
Reading materials: Relevant papers as handed out at each lecture.
