Search Algorithms in 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 | 14 Pages |
| File Size | 229.63 KB |
| Views | 3091 |
| Downloads | 1 |
| Price: |
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Description
Buy "Search Algorithms in Artificial Intelligence" now and learn how to harness the power of AI to solve complex problems efficiently. This comprehensive book delves into various search algorithms, from uninformed (blind) search to informed (heuristic) search strategies. With clear explanations and detailed examples, you'll explore techniques such as breadth-first search, depth-first search, uniform-cost search, and the powerful A* search. Perfect for beginners and experienced professionals alike, this book provides valuable insights into the mechanisms that drive intelligent agents.
Discover the fascinating world of problem-solving agents, search spaces, and the intricacies of search trees. The book covers essential concepts like path cost, transition models, and optimal solutions, making it an indispensable resource for anyone looking to deepen their understanding of AI's core methodologies. "Search Algorithms in Artificial Intelligence" equips you with the knowledge to tackle real-world challenges using state-of-the-art search techniques, ensuring you stay ahead in the ever-evolving field of AI.
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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