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Artificial Intelligence

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Course type: Online Instructor led Course

Platform: Google hangout

Course ID: 5881

Course type: Online Instructor led Course

Platform: Google hangout

Students Interested 2 (Seats Left 0)

No Reviews Yet

About the Course

Artificial Intelligence

Topics Covered

3 1 0 4
Intelligent agents * Agents and environments * Good behavior * The nature of
environments * Structure of agents * Problem solving * Problem solving agents *
Example problems * Searching for solutions * Uniformed search strategies *
Avoiding repeated states * Searching with partial information.

Informed search and exploration * Informed search strategies * Heuristic function *
Local search algorithms and optimistic problems * Local search in continuous spaces
* Online search agents and unknown environments * Constraint Satisfaction
Problems(CSP) * Backtracking Search and Local Search for CSP * Structure of
problems * Adversarial search * Games * Optimal decisions in games * Alpha-Beta
pruning * Imperfect real-time decision * Games that include an element of chance.

First order logic * Representation revisited * Syntax and semantics for first order
logic * Using first order logic * Knowledge engineering in first order logic *
Inference in first order logic * Propositional versus first order logic * Unification and
lifting * Forward chaining * Backward chaining * Resolution * Knowledge
representation * Ontological engineering * Categories and objects * Actions *
Simulation and events * Mental events and mental objects.

Learning from observations * Forms of learning * Inductive learning * Learning
decision trees * Ensemble learning * Knowledge in learning * Logical formulation of learning * Explanation based learning * Learning using relevant information *
Inductive logic programming * Statistical Learning Methods * Learning with
Complete Data * Learning with Hidden Variable * EM Algorithm * Instance Based
Learning * Neural Networks * Reinforcement Learning * Passive Reinforcement
Learning * Active reinforcement learning * Generalization in reinforcement learning.

Communication * Communication as action * Formal grammar for a fragment of
english * Syntactic analysis * Augmented grammars * Semantic interpretation *
Ambiguity and disambiguation * Discourse understanding * Grammar Induction *
Probabilistic language processing * Probabilistic language models * Information
Retrieval * Information extraction * Machine translation.

Who should attend

Engineering students people interested in learning Robotics



What you need to bring

Pen, Note Book

Key Takeaways

Knowledge on Artificial Intelligence

Date and Time

Not decided yet.

About the Trainer

Sivakumar C picture

Avg Rating

0 Reviews

0 Students

1 Courses

Sivakumar C

B.E., (M.Tech)

10 Years of Experience

20 years - 14 years in Industry, 6 years in Teaching


No reviews currently Be the First to Review


Students Interested 2 (Seats Left 0)

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