Course Name: Fuzzy Sets, Logic and Systems & Applications

Course abstract

The course is designed to give a solid grounding of fundamental concepts of fuzzy logic and its applications. The level of the course is chosen to be such that all students aspiring to be a part of computational intelligence directly or indirectly in near future should get these concepts.


Course Instructor

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Prof. Nishchal Kumar Verma

Nishchal K Verma (SM13) is Professor, Department of Electrical Engineering and Inter-disciplinary Program in Cognitive Science, Indian Institute of Technology Kanpur, India. He obtained his PhD in Electrical Engineering from Indian Institute of Technology Delhi, India. He is an awardee of Devendra Shukla Young Faculty Research Fellowship by Indian Institute of Technology Kanpur, India for year 2013-16. His research interests include big data analysis, deep learning of neural and fuzzy networks, machine learning algorithms, computational intelligence, computer vision, brain computer/machine interface, intelligent informatics, soft-computing in modelling and control, internet of things/ cyber physical systems, cognitive science and intelligent fault diagnosis systems, prognosis and health management. He has authored more than 200 research papers. Dr. Verma is an IETE Fellow. He is currently serving as Guest Editor of the IEEE Access special section Advance in Prognostics and System Health Management, an Editor of the IETE Technical Review Journal, an Associate Editor of the IEEE Transactions on Neural Networks and Learning Systems, an Associate Editor of the IEEE Computational Intelligence Magazine, an Associate Editor of the Transactions of the Institute of Measurement and Control, U.K. and Editorial Board Member for several journals and conferences.
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 Course Duration : Jan-Apr 2022

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 Enrollment : 14-Nov-2021 to 31-Jan-2022

 Exam registration : 13-Dec-2021 to 18-Mar-2022

 Exam Date : 23-Apr-2022

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Enrollment Statistics

Total Enrollment: 2771

Assignment Statistics




Score Distribution Graph - Legend

Assignment Score: Distribution of average scores garnered by students per assignment.
Exam Score : Distribution of the final exam score of students.
Final Score : Distribution of the combined score of assignments and final exam, based on the score logic.