Machine Learning detailed Syllabus for Electronics & Communication Engineering (ECE), 2018 scheme has been taken from the VTUs official website and presented for the VTU students. For Course Code, Subject Names, Teaching Department, Paper Setting Board, Theory Lectures, Tutorial, Practical/Drawing, Duration in Hours, CIE Marks, Total Marks, Credits and other information, visit full semester subjects post given below. The Syllabus PDF files can also be downloaded from the official website of the university.
For all the other VTU ECE 7th Sem Syllabus for BE 2018 Scheme, visit Electronics & Communication Engineering 7th Sem 2018 Scheme.
For all the (Professional Elective-3) subjects refer to Professional Elective-3 Scheme. The detail syllabus for machine learning is as follows.
Course Learning Objectives:
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Module – 1
Introduction: Well posed learning problems, Designing a Learning system, Perspective and Issues in Machine Learning. Concept Learning: Concept learning task, Concept learning as search, Find-S algorithm, Version space, Candidate Elimination algorithm, Inductive Bias. Python libraries suitable for Machine Learning: Numerical Analysis and Data Exploration with NumPy Arrays, and Data Visualization with Matplotlib Text Book1, Sections: 1.1 – 1.3, 2.1-2.5, 2.7 10 Hours
Module – 2
Decision Tree Learning: Decision tree representation, Appropriate problems for decision tree learning, Basic decision tree learning algorithm, hypothesis space search in decision tree learning, Inductive bias in decision tree learning, Issues in decision tree learning. Example program in Python Text Book1, Sections: 3.1-3.7 10 Hours
Module – 3
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Module – 4
Bayesian Learning: Introduction, Bayes theorem, Bayes theorem and concept learning, ML and LS error hypothesis, ML for predicting probabilities, MDL principle, Naive Bayes classifier, Bayesian belief networks, EM algorithm, Example program in Python. Text book 1, Sections: 6.1 – 6.6, 6.9, 6.11, 6.12 10 Hours
Module – 5
Evaluating Hypothesis: Motivation, Estimating hypothesis accuracy, Basics of sampling theorem, General approach for deriving confidence intervals, Difference in error of two hypothesis, Comparing learning algorithms. Instance Based Learning: Introduction, k-nearest neighbor learning, locally weighted regression, radial basis function, cased-based reasoning, Reinforcement Learning: Introduction, Learning Task, Q Learning Example program in Python. Text book 1, Sections: 5.1-5.6, 8.1-8.5, 13.1-13.3 12 Hours
Course Outcomes:
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Question Paper Pattern:
- The question paper will have ten questions.
- There will be 2 questions from each module.
- Each question will have questions covering all the topics under a module.
- The students will have to answer 5 full questions, selecting one full question from each module.
Text Books:
- Tom M. Mitchell, Machine Learning, India Edition 2013, McGraw Hill Education.
Reference Books:
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For the detail Syllabus of all other subjects of BE (ECE) 7th Sem, visit Electronics & Communication Engineering 7th Sem Subjects.
For all (CBSE & Non-CBSC) BE/B.Tech results, visit VTU BE/B.Tech all semester results.