Techniques of knowledge representation
| Institution | Jomo Kenyatta University of Science and Technology |
| Course | Information Technol... |
| Year | 3rd Year |
| Semester | Unknown |
| Posted By | Jeff Odhiambo |
| File Type | |
| Pages | 7 Pages |
| File Size | 1.11 MB |
| Views | 3451 |
| Downloads | 0 |
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Description
Techniques of knowledge representation in Artificial Intelligence (AI) define how information is structured and processed for reasoning and decision-making. The main techniques include logical representation, which uses formal logic to express facts and rules; semantic networks, which represent knowledge as interconnected nodes and relationships; frames, which organize knowledge into structured templates with attributes and values; and production rules, which use "if-then" statements for decision-making. Ontologies provide a hierarchical structure of concepts and their relationships, enabling AI to understand context. These techniques help AI systems efficiently store, retrieve, and apply knowledge, making them crucial for expert systems, natural language processing, and intelligent decision-making.
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SMA 2371: Partial Differential Equations (Complete Notes, Examples & Solutions) – JKUAT Biostatistics Year 2 Semester 2
These are comprehensive and well-organized lecture notes for SMA 2371: Partial Differential Equations (PDE) offered to BSc. Biostatistics Year 2 Semester 2 students at JKUAT. The notes are neatly arranged from lecture one to the final topics, making them ideal for class learning, revision, CAT preparation, and final examinations.
The notes include detailed explanations, worked examples, step-by-step mathematical derivations, solved exercises, and applications of Partial Differential Equations. Topics covered include:
• Review of basic concepts and partial derivatives
• Jacobians, surfaces and curves in three dimensions
• Simultaneous first-order differential equations
• Methods of solving symmetric differential equations
• Orthogonal trajectories
• Pfaffian differential equations
• Linear first-order partial differential equations
• Formation of PDEs
• Elimination of arbitrary constants and arbitrary functions
• Heat, Wave, Laplace and Poisson equations
• Separation of variables
• Fourier and Laplace Transform methods
• Numerous worked examples, tutorial questions and examination-style problems with solutions.
These notes are suitable for JKUAT students and other university students studying Mathematics, Statistics, Biostatistics, Engineering, Applied Mathematics or related courses. They are an excellent revision resource for CATs and final examinations.
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