BDIC3002J Intelligent Information Proces

Academic Year 2024/2025

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


Autonomous Student Learning




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
Additional Information:
This module is delivered overseas and is not available to students based at the UCD Belfield or UCD Blackrock campuses

Assessment Strategy  
Description Timing Component Scale Must Pass Component % of Final Grade In Module Component Repeat Offered
Exam (In-person): 2 Hour Final Exam Week 15 Graded Yes



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
Name Role
Enchang Sun Tutor
Wenying Wu Tutor