Subsets of Artificial Intelligence
| Institution | Jomo Kenyatta University of Science and Technology |
| Course | Information Technolo... |
| Year | 3rd Year |
| Semester | Unknown |
| Posted By | Jeff Odhiambo |
| File Type | |
| Pages | 7 Pages |
| File Size | 225.12 KB |
| Views | 4247 |
| Downloads | 1 |
| Price: |
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Description
Buy "Subsets of Artificial Intelligence" now and learn about the diverse and fascinating subsets that make up the field of AI. This comprehensive book explores key areas such as machine learning, deep learning, natural language processing, expert systems, robotics, machine vision, and speech recognition. With clear explanations and practical examples, you'll gain insights into how these subsets work together to create intelligent systems that can learn, understand, and interact with the world around them. Perfect for both beginners and seasoned professionals, this book provides valuable knowledge on the mechanisms driving AI.
Discover how machine learning algorithms allow systems to learn from historical data, how deep learning mimics the human brain's neural networks, and how natural language processing enables computers to understand human language. The book also covers the application of AI in robotics, expert systems, and machine vision, providing a thorough understanding of each subset's role and significance. "Subsets of Artificial Intelligence" is an essential read for anyone looking to deepen their knowledge of AI and its various components.
Below is the document preview.
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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