IT8601 Computational Intelligence(CI)
Stuff Sector
Prepared by
S.Santhosh (Admin)
Important questions
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UNIT -1
1.Genetic and A*algorithm
2.Game Playing,Alpha-Beta Pruning
3.Forward Chaining and Backward Chaining
3.Forward Chaining and Backward Chaining
UNIT-2
1.Reasoning Systems for Categories,Reasoning with Default Information
2. Forward Chaining,Backward Chaining
3.Ontological Engineering.Categories and Objects
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UNIT-3
1.Fuzzy Logic,rules,inference
2.Neural Networks,Neuro-fuzzy Inference
2.Neural Networks,Neuro-fuzzy Inference
UNIT-4
1.Bayes Rule and its Applications,Bayesian Networks
2.Exact and Approximate Inference in Bayesian Networks
3.Hidden Markov Models
4.RAre Statical learning
UNIT-5
1. Information Retrieval,Information Extraction
2.Machine Learning,Symbol-Based,Connectionist
3.Morphological Analysis,Syntax analysis
**Very important questions are bolded and may be asked based on this topic
PART-C
1.Compulsory Questions {a case study where the student will have to read and analyse the subject }
mostly asked from unit (OR) a Problem given
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Share it as link alone . don't share it as screenshot or any text material if u found this anywhere kindly report me . #Admin WhatsAppContact uS
*These questions are expected for the exams This may or may not be asked for exams All the best.... from admin Santhosh
Thanks for your love and support guys keep supporting and share let the Engineers know about Us and leave a comment below for better improvements If there is any doubt feel free to ask me I will clear if I can or-else I will say some solutions ..get me through WhatsApp for instant updates ~$tuff$£ctor
Contact uS
*These questions are expected for the exams This may or may not be asked for exams
All the best.... from admin Santhosh
Thanks for your love and support guys keep supporting and share let the Engineers know about Us and leave a comment below for better improvements
If there is any doubt feel free to ask me I will clear if I can or-else I will say some solutions ..get me through WhatsApp for instant updates ~$tuff$£ctorSYLLABUS
UNIT I INTRODUCTION
Introduction to Artificial Intelligence-Search-Heuristic Search-A* algorithm-Game Playing- Alpha-Beta Pruning-Expert systems-Inference-Rules-Forward Chaining and Backward Chaining- Genetic Algorithms.
UNIT II KNOWLEDGE REPRESENTATION AND REASONING
Proposition Logic — First Order Predicate Logic — Unification — Forward Chaining -Backward Chaining — Resolution — Knowledge Representation — Ontological Engineering — Categories and Objects — Events — Mental Events and Mental Objects — Reasoning Systems for Categories — Reasoning with Default Information — Prolog Programming.
UNIT III UNCERTAINTY
Non monotonic reasoning-Fuzzy Logic-Fuzzy rules-fuzzy inference-Temporal Logic-Temporal Reasoning-Neural Networks-Neuro-fuzzy Inference.
UNIT IV LEARNING
Probability basics — Bayes Rule and its Applications — Bayesian Networks — Exact and Approximate Inference in Bayesian Networks — Hidden Markov Models — Forms of Learning — Supervised Learning — Learning Decision Trees — Regression and Classification with Linear Models — Artificial Neural Networks — Nonparametric Models — Support Vector Machines — Statistical Learning — Learning with
Complete Data — Learning with Hidden Variables- The EM Algorithm — Reinforcement Learning
UNIT V INTELLIGENCE AND APPLICATIONS
Natural language processing-Morphological Analysis-Syntax analysis-Semantic Analysis-AIl applications — Language Models — Information Retrieval — Information Extraction — Machine Translation — Machine Learning — Symbol-Based — Machine Learning: Connectionist — Machine Learning
UNIT I INTRODUCTION
Introduction to Artificial Intelligence-Search-Heuristic Search-A* algorithm-Game Playing- Alpha-Beta Pruning-Expert systems-Inference-Rules-Forward Chaining and Backward Chaining- Genetic Algorithms.
UNIT II KNOWLEDGE REPRESENTATION AND REASONING
Proposition Logic — First Order Predicate Logic — Unification — Forward Chaining -Backward Chaining — Resolution — Knowledge Representation — Ontological Engineering — Categories and Objects — Events — Mental Events and Mental Objects — Reasoning Systems for Categories — Reasoning with Default Information — Prolog Programming.
UNIT III UNCERTAINTY
Non monotonic reasoning-Fuzzy Logic-Fuzzy rules-fuzzy inference-Temporal Logic-Temporal Reasoning-Neural Networks-Neuro-fuzzy Inference.
UNIT IV LEARNING
Probability basics — Bayes Rule and its Applications — Bayesian Networks — Exact and Approximate Inference in Bayesian Networks — Hidden Markov Models — Forms of Learning — Supervised Learning — Learning Decision Trees — Regression and Classification with Linear Models — Artificial Neural Networks — Nonparametric Models — Support Vector Machines — Statistical Learning — Learning with
Complete Data — Learning with Hidden Variables- The EM Algorithm — Reinforcement Learning
UNIT V INTELLIGENCE AND APPLICATIONS
Natural language processing-Morphological Analysis-Syntax analysis-Semantic Analysis-AIl applications — Language Models — Information Retrieval — Information Extraction — Machine Translation — Machine Learning — Symbol-Based — Machine Learning: Connectionist — Machine Learning