Artificial Intelligence

Unlock the potential of artificial engineering with this comprehensive guide, tailored for students and professionals eager to excel in AI. Explore key topics such as machine learning, algorithms, and neural networks, complete with practical examples and projects. Enhance your expertise in building intelligent systems and solving real-world challenges. Ideal for anyone looking to advance their career in the booming field of artificial intelligence. Elevate your knowledge and stay competitive in today’s tech-driven landscape.

• State Spaces
• Graph Searching
• A∗ Search
• A Generic Searching Algorithm
• Genetic Algorithms
• Breadth-first Search
• Heuristic Search
• Games
• Backtracking
• Minimax Algorithm
• Uninformed Search Strategies
• Properties Of Heuristics
• Depth First Search
• N-queens Eample
• Optimal Decisions In Games
• Proof Of Admissibility Of A*
• Search Tree
• Alpha Beta Pruning
• Look Ahead
• Iterative-deepening A*
• Greedy Search
• Search Graphs
• Informed Search Strategies
• Bi-directional Search
• Consistency Driven Techniques
• Adversarial Search
• Path Consistency (K-consistency)
• Methods Of Informed Search
• Other Memory Limited Heuristic Search
• Properties Of Depth First Search
• Reinforcement Learning
• Passive Reinforcement Learning
• Semantics Of Bayesian Networks
• Decision Trees
• Supervised Learning
• Learning Issues
• Semantic Networks
• Neural Networks
• Naive Bayes Models
• Inference In A Semantic Net
• Artificial Neural Networks
• Review Of Probability Theory
• Decision Tree Construction
• Knowledge Representation Formalisms
• Probabilistic Reasoning
• Learning Hidden Markov Models
• Frames
• Reinforcement Learning
• Decision Tree Pruning
• Perceptron
• Statistical Learning
• Problem Solving Vs. Planning
• Introduction To Learning
• Candidate Elimination Algorithm
• Learning With Complete Data
• Learning Bayesian Networks With Hidden Variables
• Introduction To Planning
• Back-propagation Algorithm
• Learning With Hidden Variables
• Bayesian Parameter Learning
• Unsupervised Clustering: Learning Mixtures Of Gaussians
• Taxonomy Of Learning Systems
• Extending Semantic Nets
• Multi-layer Perceptrons
• Perceptron Learning
• Passive Reinforcement Learning
• Concept Learning
• The Candidate-elimination Algorithm
• Splitting Functions
• Interleaving Vs. Non-interleaving Of Sub-plan Steps
• Planning As Search
• Interpreting Frames
• Logic Based Planning
• Partial-order Planning
• Algorithm To Find A Maximally-specific Hypothesis
• Simple Sock/shoe Example
• Slots As Objects
• Mathematical Formulation Of The Inductive Learning Problem
• Planning Systems
• Learning Bayes Net Structures
• Situation-space Planning Algorithms
• Plan-space Planning Algorithms
• Concept Learning As Search
• Maximum-likelihood Parameter Learning: Continuous Models
• The General Form Of The Em Algorithm
• Propositional Logic
• Rules Of Inference
• Syntax Of Propositional Calculus
• Hidden Markov Model
• Bayesian Networks
• Knowledge Representation And Reasoning
• Some Proof Strategies
• Forward Chaining
• First Order Logic
• And/or Trees
• Semantics
• Propositional Logic Inference
• Knowledge-level Debugging
• Rule Based Systems
• Pure Prolog
• Unification
• Propositional Definite Clauses
• Herbrand Universe
• Soundness, Completeness, Consistency, Satisfiability
• Non-monotonic Reasoning
• Resolution
• Choice Between Forward And Backward Chaining
• Soundness And Completeness
• Proof As Search
• Backward Chaining
• Herbrand Revisited
• Truth Maintenance Systems
• Turing Test
• Introduction To Artificial Intelligence
• History Of Ai
• Knowledge Representation
• Typical Ai Problems
• The Ai Cycle
• Limits Of Ai
• Introduction To Agents
• Intelligent Agents
• Structure Of Intelligent Agents
• Search For Solutions
• Types Of Agent Program
• Agent Performance
• Agent Architectures
• Goal Based Agents
• Agents And Environments
• Utility-based Agents
• Regression Algorithms
• Natural Language Processing
• Clustering Algorithms
• Statistical Algorithms
• Pattern Recognition Methodologies
• Pattern Recognition Algorithms
• Introduction To Pattern Recognition
• Natural Language Understanding
• Natural Language Generation
• Usage & Application Pattern Recognition
• Ambiguity
• Steps In Language Understanding And Generation
• Sequence Labeling Algorithms