BDIC3002J Intelligent Information Proces

Academic Year 2021/2022

Intelligent Information Processing is a method that transfer the incomplete, unreliable, inaccurate, inconsistent and uncertain knowledge or information to complete, reliable, accurate, consistent and certain knowledge and information. It involves multiple areas of information science, modern signal processing theory and methods of artificial neural networks, fuzzy theory, artificial intelligence applications, and it is also an evolving discipline. As a subject elective course, the goal of this course is through this study, students can understand the basic concepts of intelligent information processing, the basic principles of intelligent information processing, the basic calculation methods to master a variety of technology integration and effective application in the future to engage in scientific lay a solid foundation for research and information processing.

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Curricular information is subject to change

Learning Outcomes:

• Students should be able to understand the basic concepts and principles of artificial intelligence and its corresponding processing algorithms.
• Be able to analysis and design A* search algorithm in a graph or ANDOR graph.
• Be able to propose question-answering system by using resolution methods and tautology.

Indicative Module Content:

Week Topic
1-2 Introduction to Intelligent Information Processing and Artificial Intelligent
3-8 Intelligent Information Search Methodologies and Algorithms
9-13 Logical Reasoning: Rules and Applications
14-15 Introduction to Knowledge Engineering
16 Revision

Student Effort Hours: 
Student Effort Type Hours




Approaches to Teaching and Learning:
active/task-based learning; peer and group work; lectures; enquiry & problem-based learning; case-based learning; student presentations, etc. 
Requirements, Exclusions and Recommendations

Not applicable to this module.

Module Requisites and Incompatibles
Not applicable to this module.
Assessment Strategy  
Description Timing Open Book Exam Component Scale Must Pass Component % of Final Grade
Examination: Final exam 2 hour End of Trimester Exam No Alternative linear conversion grade scale 60% (Chinese modules) No


Attendance: Tutorial questions and attendance Throughout the Trimester n/a Alternative linear conversion grade scale 60% (Chinese modules) No


Class Test: Quiz Throughout the Trimester n/a Alternative linear conversion grade scale 60% (Chinese modules) No


Carry forward of passed components
Remediation Type Remediation Timing
In-Module Resit Prior to relevant Programme Exam Board
Please see Student Jargon Buster for more information about remediation types and timing. 
Feedback Strategy/Strategies

• Feedback individually to students, post-assessment
• Group/class feedback, post-assessment
• Self-assessment activities

How will my Feedback be Delivered?

Not yet recorded.

• Pathak, Nishith: Artificial Intelligence for .Net: Speech, Language, and Search. Apress, 2017.
• Musen, Mark; Studer, Rudi; Neumann, Bernd: Intelligent Information Processing, Springer
Timetabling information is displayed only for guidance purposes, relates to the current Academic Year only and is subject to change.

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