Artificial Neural Networks detail syllabus for Electronics & Communication Engineering (ECE), 2017 scheme is taken from VTU official website and presented for VTU students. The course code (17EC653), and for exam duration, Teaching Hr/week, Practical Hr/week, Total Marks, internal marks, theory marks, duration and credits do visit complete sem subjects post given below.
For all other ece 6th sem syllabus for be 2017 scheme vtu you can visit ECE 6th Sem syllabus for BE 2017 Scheme VTU Subjects. For all other Professional Elective-2 subjects do refer to Professional Elective-2. The detail syllabus for artificial neural networks is as follows.
Course Objectives:
The objectives of this course are:
- Understand the basics of ANN and comparison with Human brain
- Provide knowledge on Generalization and function approximation and various architectures of building an ANN
- Provide knowledge of reinforcement learning using neural networks
- Provide knowledge of unsupervised learning using neural networks.
Module 1
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Module 2
Supervised Learning: Perceptron learning and Non Separable sets, a-Least Mean Square Learning, MSE Error surface, Steepest Descent Search, ^-LMS approximate to gradient descent, Application of LMS to Noise Cancelling, Multi-layered Network Architecture, Backpropagation Learning Algorithm, Practical consideration of BP algorithm.
Module 3
Support Vector Machines and Radial Basis Function: Learning from Examples, Statistical Learning Theory, Support Vector Machines, SVM application to Image Classification, Radial Basis Function Regularization theory, Generalized RBF Networks, Learning in RBFNs, RBF application to face recognition.
Module 4
For complete syllabus and results, class timetable and more pls download iStudy. Its a light weight, easy to use, no images, no pdfs platform to make students life easier.
Module 5
Self-organization Feature Map: Maximal Eigenvector Filtering, Extracting Principal Components, Generalized Learning Laws, Vector Quantization, Self-organization Feature Maps, Application of SOM, Growing Neural Gas.
Course Outcomes:
At the end of the course, students should be able to:
- Understand the role of neural networks in engineering, artificial intelligence, and cognitive modelling.
- Understand the concepts and techniques of neural networks through the study of the most important neural network models.
- Evaluate whether neural networks are appropriate to a particular application.
- Apply neural networks to particular applications, and to know what steps to take
Text Books:
Neural Networks A Classroom Approach- Satish Kumar, McGraw Hill Education (India) Pvt. Ltd, Second Edition.
Reference Books:
- Introduction to Artificial Neural Systems-J.M. Zurada, Jaico Publications 1994.
- Artificial Neural Networks-B. Yegnanarayana, PHI, New Delhi 1998.
For detail syllabus of all other subjects of BE Ece, 2017 regulation do visit Ece 6th Sem syllabus for 2017 Regulation.
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