Artificial Neural Network detailed syllabus scheme for Electronics & Electrical Engineering (EL), 2017 regulation has been taken from the MU official website and presented for the Bachelor of Engineering students. For Course Code, Course Title, Test 1, Test 2, Avg, End Sem Exam, Team Work, Practical, Oral, Total, and other information, do visit full semester subjects post given below.
For 7th Sem Scheme of Electronics & Electrical Engineering (EL), 2017 Pattern, do visit EL 7th Sem Scheme, 2017 Pattern. For the Elective-1 scheme of 7th Sem 2017 regulation, refer to EL 7th Sem Elective-1 Scheme 2017 Pattern. The detail syllabus for artificial neural network is as follows.
Artificial Neural Network Syllabus for Electronics & Electrical Engineering BE 7th Sem 2017 Pattern Mumbai University
Course Objectives:
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1. Artificial Neural Systems:
Preliminaries Neural Computation: Some Examples and Applications History of Artificial Neural Systems Development 6
2. Fundamental Concepts and Models of Artificial Neural Systems
Biological Neurons and Their Artificial Models Models of Artificial Neural Networks Neural Processing Learning and Adaptation Neural Network Learning Rules
3. Single-Layer Perceptron Classifiers
Classification Model, Features, and Decision Regions Discriminant Functions Linear Machine and Minimum Distance Classification Nonparametric Training Concept Training and Classification Using the Discrete Perceptron Single-Layer Continuous Perceptron Networks for Linearly Separable Classifications Multicategory Single-Layer Perceptron Networks 10
4. Multilayer Feedforward Networks
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5. Single-Layer Perceptron Classifier
Classification Model, Features, and Decision Regions Discriminant Functions Linear Machine and Minimum Distance Classification Nonparametric Training Concept Training and Classification Using the Discrete Perceptron Single-Layer Continuous Perceptron Networks for Linearly Separable Classifications Multicategory Single-Layer Perceptron Networks 10
6. Multilayer Feedforward Networks
Linearly Nonseparable Pattern Classification Delta Learning Rule for Multiperceptron Layer Generalized Delta Learning Rule Learning Factors Classifying and Expert Layered Networks Functional Link Networks 10
Assessment:
Two tests must be conducted which should cover at least 80% of syllabus. The average marks of both the test will be considered as final IA marks
End Semester Examination:
For the complete Syllabus, results, class timetable, and many other features kindly download the iStudy App
It is a lightweight, easy to use, no images, and no pdf platform to make students’s lives easier..
Text Books:
- LaureneV. Fausett, Fundamentals of Neural Networks-Architectures, Algorithms and Applications, Pearson Education, 2011.
- Jacek M. Zurada, Introduction to artificial neural systems, West Publishing Company
Reference Books:
- James. A. Freeman and David.M.Skapura, “Neural Networks Algorithms, Applications and Programming Techniques “,Pearson Education, Sixth Reprint, 2011.
- Simon Haykin, “Neural Networks and Learning Methods, PHI Learning Pvt. Ltd., 2011.
- James A. Anderson, An Introduction to Neural Networks, PHI Learning Pvt. Ltd., 2011.
- Martin T. Hagan, Howard B. Demuth, Mark Beale, Neural Network Design, Cengage Learning, Fourth Indian Reprint, 2010.
- Bart Kosko, Neural Networks and Fuzzy Systems-A Dynamical Approach to Machine Intelligence, PHI Learning Pvt. Ltd., 2010.
For detail Syllabus of all subjects of Electronics & Electrical Engineering (EL) 7th Sem 2017 regulation, visit EL 7th Sem Subjects of 2017 Pattern.