Artificial Intelligence (AI)

What is Artificial quality (AI) ?

Artificial quality (AI) refers to the simulation of quality quality successful machines that are programmed to deliberation similar humans and mimic their actions. The word whitethorn besides beryllium applied to immoderate instrumentality that exhibits traits associated with a quality caput specified arsenic learning and problem-solving.

Artificial quality (AI) is an country of machine subject that emphasizes the instauration of intelligent machines that enactment and respond similar humans. The processes see learning, reasoning and self-correction. AI is accomplished by studying however quality encephalon thinks, and however humans learn, decide, and enactment portion trying to lick a problem.

Discover however to physique intelligent applications centered connected images, text, and clip bid data. It is utilized extensively crossed galore fields specified arsenic hunt engines, representation recognition, robotics, finance, and truthful on. You'll larn astir assorted algorithms that tin beryllium utilized to physique Artificial Intelligence apps.

What is for you ?

- Introduction to Artificial Intelligence and intelligent agents, past of Artificial Intelligence
- Building intelligent agents (search, games, logic, constraint restitution problems)
- Machine Learning algorithms
- Applications of AI (Natural Language Processing, Robotics/Vision, Language Understanding)

App contents

1) Introduction to AI

- Turing Test
- History of Artificial Intelligence
- Typical Artificial Intelligence problem
- The Artificial Intelligence Cycle

2) Problem solving attack AI

- State Space
- Graph Searching
- A* search
- A Generic Search
- Genetic Algorithm
- Breadth-First Search
- Depth Search
- Heuristic Search
- Games
- Backtracking
- Minimax Algorithm
- Uninformed Search
- N-Queen sample
- Optimal Decision
- Proof of Admissibility
- Search Tree
- Alpha Beta Pruning
- Look Ahead
- Iterative-Deepening
- Greedy Search
- Search Graph
- Informed Search
- Bi-directional Search
- Consistency driven
- Adversarial Search
- Path consistency
- Method of Informed
- Other Memory limited
- Properties of depth

3) Knowledge and reasoning

- Propositional Logic
- Rule of Inference
- Hidden Markov Model
- Bayesian networks
- Forward chaining
- First bid logic
- AND/OR Trees
- Semantics
- Knowledge Level
- Rule based systems
- Pure Pro-log
- Unification
- Herbrand Universe
- Soundness
- Non-Monotonic

4) Acting logically and learning

- Reinforced Learning
- Semantics of Bayesian
- Supervised Learning
- Learning issue
- Semantic Networks
- Neural Network
- Native Bayes Model
- Artificial Neural
- Probabilistic
- Frames
- Decision Tree Pruning
- Perceptron
- Statistical learning
- Candidate Elimination
- Back-Propagation
- Unsupervised
- Taxonomy of Learning
- Extending Semantic
- Multi-Layer
- Splitting Functions
- Interleaving vs. Non-Interleaving of Sub-Plan
- Planning arsenic search
- The General signifier of EM Algorithm

5) Communicating, perceiving and acting

- Regression Algorithm
- Natural Language
- Clustering Algorithm
- Statistical Algorithm
- Pattern Recognition
- Usage & Application
- Ambiguity
- Steps successful Language

These 5 units contains 142 topics and by speechmaking each you volition beryllium bully capable to plan a strategy utilizing languages similar R, Python, SAS, Matlab, Weka, SPSS etc.

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