CS702(A) Computational Intelligence complete study material for RGPV CSE 7th Semester. Download unit-wise notes, important questions, PYQ analysis and exam resources for Fuzzy Systems, Genetic Algorithms, Rough Set Theory, Hidden Markov Models, Decision Trees and Swarm Intelligence.
Computational Intelligence deals with intelligent algorithms inspired by nature, human reasoning and learning systems. This subject covers fuzzy logic, genetic algorithms, rough sets, hidden Markov models, decision trees and swarm-based optimization techniques.
Study training models, parametric models, non-parametric models and multilayer networks.
Learn genetic algorithms, reproduction, crossover, mutation and swarm intelligence.
Understand fuzzy systems, rough sets, HMM, decision trees and optimization methods.
Open any unit to access detailed notes, important questions and PYQ analysis.
Introduction, types, components, learning/training model, parametric models, non-parametric models and multilayer networks.
Fuzzy set theory, fuzzy sets, operations, membership functions, fuzzy relations, fuzzy measures, fuzzy rules, inferencing and fuzzy control.
Basic genetics, concepts, working principle, creation of offsprings, encoding, fitness function, selection, reproduction, crossover and mutation.
Rough set theory, fundamental concepts, set approximation, rough membership, attributes, optimization, hidden Markov models and decision tree model.
Introduction to swarm intelligence, ant colony optimization, particle swarm optimization, bee colony optimization and applications.
Complete syllabus of CS702(A) Computational Intelligence for RGPV CSE 7th Semester.
Introduction to Computational Intelligence, types of Computational Intelligence, components of Computational Intelligence, concept of learning/training model, parametric models, non-parametric models, multilayer networks, feed forward network and feedback network.
Fuzzy systems, fuzzy set theory, fuzzy sets and operations, membership functions, fuzzy relations and their composition, fuzzy measures, fuzzy logic, fuzzy rules, inferencing, fuzzy control, fuzzification, rule-based design and defuzzification.
Genetic algorithms, basic genetics, concepts, working principle, creation of offsprings, encoding, fitness function, selection functions, genetic operators including reproduction, crossover and mutation, genetic modeling and benefits.
Rough set theory, introduction, fundamental concepts, set approximation, rough membership, attributes, optimization, hidden Markov models and decision tree model.
Introduction to swarm intelligence, swarm intelligence techniques, ant colony optimization, particle swarm optimization, bee colony optimization and applications of Computational Intelligence.
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Most important questions for 7 marks and 14 marks answers.
| Topic | Unit | Expected Frequency | Importance |
|---|---|---|---|
| Computational Intelligence Basics | Unit 1 | High | ⭐⭐⭐⭐ |
| Parametric & Non-Parametric Models | Unit 1 | Medium | ⭐⭐⭐ |
| Fuzzy Sets & Membership Functions | Unit 2 | Very High | ⭐⭐⭐⭐⭐ |
| Fuzzification & Defuzzification | Unit 2 | Very High | ⭐⭐⭐⭐⭐ |
| Genetic Algorithm Working | Unit 3 | Very High | ⭐⭐⭐⭐⭐ |
| Crossover & Mutation | Unit 3 | High | ⭐⭐⭐⭐ |
| Rough Set Theory | Unit 4 | High | ⭐⭐⭐⭐ |
| Hidden Markov Model | Unit 4 | High | ⭐⭐⭐⭐ |
| Ant Colony Optimization | Unit 5 | Very High | ⭐⭐⭐⭐⭐ |
| Particle Swarm Optimization | Unit 5 | Very High | ⭐⭐⭐⭐⭐ |
Computational Intelligence is a branch of AI that uses learning, reasoning and nature-inspired algorithms to solve complex problems.
Main topics include fuzzy systems, genetic algorithms, rough set theory, hidden Markov models, decision trees and swarm intelligence.
Fuzzy logic handles uncertainty and approximate reasoning using membership values between 0 and 1.
Genetic Algorithm is a search and optimization technique inspired by natural selection and genetics.
Swarm Intelligence is inspired by collective behavior of ants, birds, bees and other natural groups.
Yes, it is scoring if you prepare definitions, algorithms, steps, diagrams and applications properly.
Architecture models, ADLs, CBAM, ATAM, ADD and documentation.
Open Software ArchitectureDeep learning, CNN, RNN, autoencoders, Q-learning and policy gradients.
Open DRLOLAP, classification, clustering, association rules, Apriori and FP Growth.
Open DMW