PROFESSIONAL ELECTIVES
Electives in Artificial Intelligence
| Course Name | Computational Intelligence |
| Course Code | 23CSE474 |
| Program | B. Tech. in Computer Science and Engineering (CSE) |
| Credits | 3 |
| Campus | Amritapuri ,Coimbatore,Bengaluru, Amaravati, Chennai |
Electives in Artificial Intelligence
Brief review – Pitfalls of Traditional AI – Why computational intelligence? – Computational Intelligence concept, Neural Networks – single layer and multilayer, Backpropagation, Radial-Basis Function Networks, Recurrent Neural Networks.
Fuzzy sets, properties, membership function, fuzzy operations. Fuzzy logic and fuzzy inference and applications.
Evolutionary computation – constituent algorithms, Collective Intelligence – Swarm intelligence algorithms – Overview of other bio-inspired algorithms
Hybrid approaches (neural networks, fuzzy logics, genetic algorithm, etc) – Applications of Computational intelligence in Industrial applications, manufacturing and logistics – Fuzzy systems and Evolutionary algorithms.
Course Objectives
Course Outcomes
CO1: Understand the nature and purpose of different computational intelligent components.
CO2: Apply neural networks and applications in real-world scenarios.
CO3: Understand fuzzy systems in application scenarios.
CO4: Analyze the working of Evolutionary algorithms in optimization problems.
CO5: Apply Evolutionary approaches to application scenarios.
CO-PO Mapping
| PO/PSO | PO1 | PO2 | PO3 | PO4 | PO5 | PO6 | PO7 | PO8 | PO9 | PO10 | PO11 | PO12 | PSO1 | PSO2 |
| CO | ||||||||||||||
| CO1 | 3 | 2 | 2 | 2 | 1 | 1 | 1 | – | – | – | – | – | 3 | 2 |
| CO2 | 3 | 2 | 2 | 2 | 2 | 2 | 2 | – | – | – | – | – | 3 | 2 |
| CO3 | 3 | 2 | 2 | 2 | 1 | 2 | 2 | – | – | – | – | – | 3 | 2 |
| CO4 | 3 | 1 | 2 | 2 | 3 | 1 | 1 | – | – | – | – | – | 3 | 2 |
| CO5 | 3 | 1 | 2 | 2 | 3 | 2 | 2 | – | – | – | – | – | 3 | 2 |
Evaluation Pattern: 70:30
| Assessment | Internal | End Semester |
| Midterm | 20 | |
| Continuous Assessment – Theory (*CAT) | 10 | |
| Continuous Assessment – Lab (*CAL) | 40 | |
| **End Semester | 30 (50 Marks; 2 hours exam) |
*CAT – Can be Quizzes, Assignments, and Reports
*CAL – Can be Lab Assessments, Project, and Report
**End Semester can be theory examination/ lab-based examination/ project presentation
Textbook(s)
David B Fogel, Derong Liu, James M Keller. “Fundamentals of Computational Intelligence: Neural Networks, Fuzzy Systems, and Evolutionary Computation”. John Wiley & Sons; 2016
Konar A, ‘Computational Intelligence: Principles, Techniques and Applications”, Springer Verlat, 2005.
Reference(s)
Siddique, Nazmul, and Hojjat Adeli. “Computational intelligence: synergies of fuzzy logic, neural networks and evolutionary computing”. John Wiley & Sons, 2013.
Lam, Hak-Keung, and Hung T. Nguyen, eds. “Computational intelligence and its applications: evolutionary computation, fuzzy logic, neural network and support vector machine techniques”. World Scientific, 2012.
Eberhart RC, Shi Y. “Computational intelligence: concepts to implementations”. Elsevier; 2007
Karray F, Karray FO, De Silva CW. “Soft computing and intelligent systems design: theory, tools, and applications. Pearson Education”, First Edition, Pearson India, 2009.
Engelbrecht AP. “Computational intelligence: an introduction”. John Wiley & Sons; 2007.
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