Artificial Intelligence

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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