人工智能

GraduateCourse Syllabus

Course Serial Number：            School(orDepartment)：ComputerScience &Engineering

I.  Teaching Goals and Requirements:

  This course is one of the core coursesof the graduated students majoring computer applications in our department andone of the elective courses of other graduated students in our department. Themain missions of the course are :

1)     Extending the students’knowledge fields,

2)     Making the studentsunderstanding the basic organization, research methodology and synthesizingmethodology of the knowledge-based systems,

3)     Letting the students knowingabout the front-end research progress in the field of artificial intelligenceand the joint point between the field of artificial intelligence and otherresearch fields,

4)     Understanding the ideas andmethodology in the field of artificial intelligence and their applications inthe related fields,

5)     Preparing the fundamentals infurther researches in the field of artificial intelligence and in the relatedfields for the students. 

The requirements that should begrasped by the students in the course are the basic concepts, basic principlesand basic methodology occurred in the course. The examination method of thiscourse is that every student should write a paper that should include the ideasin the course and relate to some real application fields. And the number ofwords occurred in the body of the paper should be more than 3000. Then thelecturer would interview with the students to exchange the ideas of theirpapers. Finally, the lecturer would give the students scores in the light oftheir papers and interviewing cases.

II.Teaching Syllabus (chapters, including sections) 

The mainlectures in the course include:

1)     The basic concepts of artificialintelligence,

2)     The research methodology in thefield of artificial intelligence,

3)     The history and trend of thefield of artificial intelligence,

4)     Knowledge and normal knowledgerepresentation and reasoning methodology,

5)     Dynamic searching and heuristicsearching,

6)     Logic in the field of artificialintelligence and related applications,

7)     Machine learning and typicalmethods in machine learning,

8)     Data mining and knowledgediscovery,

9)     Distributed artificialintelligence and multi-agent systems,

10)  Artificial intelligence and electronic business

III. TeachingCalendar：

Note: The teaching calendar is not compulsorytemporarily for courses for doctor degree.