Machine Learning Telecom 8th Sem Syllabus for VTU BE 2017 Scheme (Professional Elective -5)

Machine Learning detail syllabus for Telecommunication Engineering (Telecom), 2017 scheme is taken from VTU official website and presented for VTU students. The course code (17EC834), 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 telecom 8th sem syllabus for be 2017 scheme vtu you can visit Telecom 8th Sem syllabus for BE 2017 Scheme VTU Subjects. For all other Professional Elective -5 subjects do refer to Professional Elective -5. The detail syllabus for machine learning is as follows.

Course Objectives:

This course will enable students to:

• Introduce some concepts and techniques that are core to Machine Learning.
• Understand learning and decision trees.
• Acquire knowledge of neural networks, Bayesian techniques and instant based learning.
• Understand analytical learning and reinforced learning.

Module 1
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Module 2

Decision Tree and ANN: Decision Tree Representation, Hypothesis Space Search, Inductive bias in decision tree, issues in Decision tree. Neural Network Representation, Perceptrons, Multilayer Networks and Back Propagation Algorithms.

Module 3

Bayesian and Computational Learning: Bayes Theorem, Bayes Theorem Concept Learning, Maximum Likelihood, Minimum Description Length Principle, Bayes Optimal Classifier, Gibbs Algorithm, Naive Bayes Classifier.

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

Analytical Learning and Reinforced Learning: Perfect Domain Theories, Explanation Based Learning, Inductive-Analytical Approaches, FOCL Algorithm, Reinforcement Learning.

Course Outcomes:

At the end of the course, students should be able to:

• Understand the core concepts of Machine learning.
• Appreciate the underlying mathematical relationships within and across Machine Learning algorithms.
• Explain paradigms of supervised and un-supervised learning.
• Recognize a real world problem and apply the learned techniques of Machine Learning to solve the problem.

Text Books:

Machine Learning-Tom M. Mitchell, McGraw-Hill Education, (Indian Edition), 2013.

Reference Books:

1. Introduction to Machine Learning- Ethem Alpaydin, 2nd Ed., PHI Learning Pvt. Ltd., 2013.
2. The Elements of Statistical Learning-T. Hastie, R. Tibshirani, J. H. Friedman, Springer; 1st edition, 2001.

For detail syllabus of all other subjects of BE Telecom, 2017 regulation do visit Telecom 8th Sem syllabus for 2017 Regulation.

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