Registry
Module Specifications
Archived Version 2021 - 2022
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Description 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. | |||||||||||||||||||||||||||||||||||||
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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 | |||||||||||||||||||||||||||||||||||||
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 |
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Other Resources None | |||||||||||||||||||||||||||||||||||||
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