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Artificial Intelligence

Brand: Ashish Kumar
MPN: com.faadooengineers.free_artificialintelligence
Category: App (Science)
Price: $0.00
Availability: Available instantly on compatible devices
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Features

  • This unique free application is useful for all students of Artificial Intelligence across the world. It covers 142 topics of Artificial Intelligence in detail. These 142 topics are divided in 5 units.
  • Each topic is around 600 words and is complete with diagrams, equations and other forms of graphical representations along with simple text explaining the concept in detail.
  • The USP of this application is "ultra-portability". Students can access the content on-the-go from any where they like.
  • Basically, each topic is like a detailed flash card and will make the lives of students simpler and easier.
  • Some of topics Covered in this application are:
  • 1. Turing test
  • 2. Introduction to Artificial Intelligence
  • 3. History of AI
  • 4. The AI Cycle
  • 5. Knowledge Representation
  • 6. Typical AI problems
  • 7. Limits of AI
  • 8. Introduction to Agents
  • 9. Agent Performance
  • 10. Intelligent Agents
  • 11. Structure Of Intelligent Agents
  • 12. Types of agent program
  • 13. Goal based Agents
  • 14. Utility-based agents
  • 15. Agents and environments
  • 16. Agent architectures
  • 17. Search for Solutions
  • 18. State Spaces
  • 19. Graph Searching
  • 20. A Generic Searching Algorithm
  • 21. Uninformed Search Strategies
  • 22. Breadth-First Search
  • 23. Heuristic Search
  • 24. AâË— Search
  • 25. Search Tree
  • 26. Depth first Search
  • 27. Properties of Depth First Search
  • 28. Bi-directional search
  • 29. Search Graphs
  • 30. Informed Search Strategies
  • 31. Methods of Informed Search
  • 32. Greedy Search
  • 33. Proof of Admissibility of A*
  • 34. Properties of Heuristics
  • 35. Iterative-Deepening A*
  • 36. Other Memory limited heuristic search
  • 37. N-Queens eample
  • 38. Adversarial Search
  • 39. Genetic Algorithms
  • 40. Games
  • 41. Optimal decisions in Games
  • 42. minimax algorithm
  • 43. Alpha Beta Pruning
  • 44. Backtracking
  • 45. Consistency Driven Techniques
  • 46. Path Consistency (K-Consistency)
  • 47. Look Ahead
  • 48. Propositional Logic
  • 49. Syntax of Propositional Calculus
  • 50. Knowledge Representation and Reasoning
  • 51. Propositional Logic Inference
  • 52. Propositional Definite Clauses
  • 53. Knowledge-Level Debugging
  • 54. Rules of Inference
  • 55. Soundness and Completeness
  • 56. First Order Logic
  • 57. Unification
  • 58. Semantics
  • 59. Herbrand Universe
  • 60. Soundness, Completeness, Consistency, Satisfiability
  • 61. Resolution
  • 62. Herbrand Revisited
  • 63. Proof as Search
  • 64. Some Proof Strategies
  • 65. Non-Monotonic Reasoning
  • 66. Truth Maintenance Systems
  • 67. Rule Based Systems
  • 68. Pure Prolog
  • 69. Forward chaining
  • 70. backward Chaining
  • 71. Choice between forward and backward chaining
  • 72. AND/OR Trees
  • 73. Hidden Markov Model
  • 74. Bayesian networks
  • 75. Learning Issues
  • 76. Supervised Learning
  • 77. Decision Trees
  • 78. Knowledge Representation Formalisms
  • 79. Semantic Networks
  • 80. Inference in a Semantic Net
  • 81. Extending Semantic Nets
  • 82. Frames
  • 83. Slots as Objects
  • 84. Interpreting frames
  • 85. Introduction to Planning
  • 86. Problem Solving vs. Planning
  • 87. Logic Based Planning
  • 88. Planning Systems
  • 89. Planning as Search
  • 90. Situation-Space Planning Algorithms
  • 91. Partial-Order Planning
  • 92. Plan-Space Planning Algorithms
  • 93. Interleaving vs. Non-Interleaving of Sub-Plan Steps
  • 94. Simple Sock/Shoe Example
  • 95. Probabilistic Reasoning
  • 96. Review of Probability Theory
  • 97. Semantics of Bayesian Networks
  • 98. Introduction to Learning
  • 99. Taxonomy of Learning Systems
  • 100. Mathematical formulation of the inductive learning problem
  • 101. Concept Learning
  • 102. Concept Learning as Search
  • 103. Algorithm to Find a Maximally-Specific Hypothesis
  • 104. Candidate Elimination Algorithm
  • 105. The Candidate-Elimination Algorithm
  • 106. Decision Tree Construction
  • 107. Splitting Functions
  • 108. Decision Tree Pruning
  • 109. Neural Networks
  • 110. Artificial Neural Networks
  • 111. Perceptron
  • 112. Perceptron Learning
  • 113. Multi-Layer Perceptrons
  • 114. Back-Propagation Algorithm
  • 115. Statistical learning

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