DCU Home | Our Courses | Loop | Registry | Library | Search DCU


Module Specifications

Archived Version 2021 - 2022

Module Title
Module Code

Online Module Resources

NFQ level 9 Credit Rating 5
Pre-requisite None
Co-requisite None
Compatibles None
Incompatibles None

This module is accredited by NUIG. This module introduces students to formal Knowledge Representation and Statistical Relational Learning. Knowledge representation and reasoning are concerned with the efficient formal representation of information and its utilization for automated problem-solving tasks. Statistical Relational Learning is an area of Artificial Intelligence and Machine Learning concerned with the representation of, and reasoning and learning with, uncertain (probabilistic) and relational domain knowledge (such as graphs, web links or symbolic facts). Planned topics: 1. Foundations of knowledge representation 2. Propositional and first-order logic 3. Foundations of reasoning 4. SAT/SMT and Answer Set Programming 5. Logic programming 6. Probabilistic logics and uncertainty reasoning. 7. Parameter and structure learning in statistical-relational settings. Further information pertaining to the module is available from NUIG.

Learning Outcomes

1. Explain the fundamental principles of knowledge representation and reasoning
2. Correctly describe and deploy the syntax and semantics of important non-probabilistic and probabilistic logics
3. Explain and decide on the appropriate use of fundamental types of and approaches to reasoning
4. Model simple application domains using logic languages and relational knowledge representation formats
5. Explain and apply fundamental principles of Machine Learning in statistical-relational settings

Workload Full-time hours per semester
Type Hours Description
Total Workload: 0

All module information is indicative and subject to change. For further information,students are advised to refer to the University's Marks and Standards and Programme Specific Regulations at: http://www.dcu.ie/registry/examinations/index.shtml

Indicative Content and Learning Activities

Assessment Breakdown
Continuous Assessment% Examination Weight%
Course Work Breakdown
TypeDescription% of totalAssessment Date
Reassessment Requirement
Resit arrangements are explained by the following categories;
1 = A resit is available for all components of the module
2 = No resit is available for 100% continuous assessment module
3 = No resit is available for the continuous assessment component
Indicative Reading List

    Other Resources

    Programme or List of Programmes