Artificial Neural Networks detail syllabus for Mechatronics (Mechatronics), 2017 scheme is taken from VTU official website and presented for VTU students. The course code (17MT755), 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 mechatronics 7th sem syllabus for be 2017 scheme vtu you can visit Mechatronics 7th Sem syllabus for BE 2017 Scheme VTU Subjects. For all other Professional Elective-IV subjects do refer to Professional Elective-IV. The detail syllabus for artificial neural networks is as follows.
Course Objectives:
Students will be able to
- Gain Knowledge of Artificial Neural Networks, Supervised Learning, Support Vector Machines and Radial Basis Function, Attractor Neural Networks, Self-organization Feature Map.
- Understand the working methodology of Artificial Neural Networks, Supervised Learning, Support Vector Machines and Radial Basis Function, Attractor Neural Networks, Self-organization Feature Map.
Module 1
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 2
Supervised Learning: Perceptron learning and Non Separable sets,a-Least Mean Square Learning, MSE Error surface, Steepest Descent Search, li-IAIS 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:
On completion of this subject, students will be able to:
- have Knowledge of Artificial Neural Networks, Supervised Learning, Support Vector Machines and Radial Basis Function, Attractor Neural Networks, Self-organization Feature Map.
- understand the working methodology of Artificial Neural Networks, Supervised Learning, Support Vector Machines and Radial Basis Function, Attractor Neural Networks, Self-organization Feature Map.
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 Mechatronics, 2017 regulation do visit Mechatronics 7th Sem syllabus for 2017 Regulation.
Dont forget to download iStudy for latest syllabus and results, class timetable and more.