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KNOWLEDGE REPRESENTATION FOR MACHINE LEARNING
This document outlines the contents of a course on knowledge representation for machine learning. It discusses introducing knowledge representation and how it has changed and evolved, including symbolic, subsymbolic, and functional representations. It also discusses machine learning techniques like neural networks. Additionally, it addresses how knowledge representation for machine learning differs from traditional knowledge representation due to the rise of machine learning methods, and the new issues knowledge representation aims to address to improve the explainability of machine learning models.
KNOWLEDGE REPRESENTATION FOR MACHINE LEARNING
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- 2. CONTENTS Introduction to KNOWLEDGEREPRESENTATION KR FOR AI , symbolic, fucnionaal, subsymbolic MACHINE LEARNING DEEP KNOWLEDGE REPRESENTATION CONVERGENCE
- 3. INTRO AND OVERVIEW ASPECIAL ISSUE * MDPI SYSTEMS 2019 A WORKSHOP, AND A TALK COURSE NOTES (GRADUATE, POSTGRADUATE) A course manual
- 4. KNOWLEDGE REPRESENTATION WHAT ISIT HOW HAS IT CHANGED What is new
- 5. KNOWLEDGE REPRESENTATION SYMBOLIC SUBSYMBOLIC BOTH ORNEITHER? FUNCTIONAL OTHER
- 6. MACHINE LEARNING NEURAL NETWORKS OTHERTECHNIQUEST
- 7. WHAT THEY MEANKR FOR ML KR FOR ML research is NOT STANDARD KR as before ML THE ISSUES (FOR EXAMPLE NEELAKANTH) CURRENT KR FOR ML tackle are different from the beginning of KR
- 8. WHAT I MEANKR FOR ML See the explainability arguments
- 9. MORE …. See lecturenotes for more details