Machine Learning detailed syllabus for Electronics & Instrumentation Engineering (EIE), R18 regulation has been taken from the JNTUHs official website and presented for the students of B.Tech Electronics & Instrumentation Engineering branch affiliated to JNTUH course structure. For Course Code, Course Titles, Theory Lectures, Tutorial, Practical/Drawing, Credits, and other information do visit full semester subjects post given below. The syllabus PDF files can also be downloaded from the universities official website.
For all the other EIE 4th Year 2nd Sem Syllabus for B.Tech R18 Regulation JNTUH scheme, visit Electronics & Instrumentation Engineering 4th Year 2nd Sem R18 Scheme.
For all the (Professional Elective-6) subjects refer to Professional Elective-6 Scheme. The detail syllabus for machine learning is as follows.
Prerequisites:
- Course on Data Structures.
- Knowledge on statistical methods.
Course Objective:
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Course Outcome:
- Understand the concepts of computational intelligence like machine learning
- Ability to get the skill to apply machine learning techniques to address the real time problems in different areas
- Understand the Neural Networks and its usage in machine learning application.
Unit – I
Introduction – Well-posed learning problems, designing a learning system, Perspectives and issues in machine learning Concept learning and the general to specific ordering – introduction, a concept learning task, concept learning as search, find-S: finding a maximally specific hypothesis, version spaces and the candidate elimination algorithm, remarks on version spaces and candidate elimination, inductive bias. Decision Tree Learning – Introduction, decision tree representation, appropriate problems for decision tree learning, the basic decision tree learning algorithm, hypothesis space search in decision tree learning, inductive bias in decision tree learning, issues in decision tree learning.
Unit – II
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Unit – III
Bayesian learning – Introduction, Bayes theorem, Bayes theorem and concept learning, Maximum Likelihood and least squared error hypotheses, maximum likelihood hypotheses for predicting probabilities, minimum description length principle, Bayes optimal classifier, Gibs algorithm, Naive Bayes classifier, an example: learning to classify text, Bayesian belief networks, the EM algorithm. Computational learning theory – Introduction, probably learning an approximately correct hypothesis, sample complexity for finite hypothesis space, sample complexity for infinite hypothesis spaces, the mistake bound model of learning. Instance-Based Learning- Introduction, k-nearest neighbour algorithm, locally weighted regression, radial basis functions, case-based reasoning, remarks on lazy and eager learning.
Unit – IV
Genetic Algorithms – Motivation, Genetic algorithms, an illustrative example, hypothesis space search, genetic programming, models of evolution and learning, parallelizing genetic algorithms. Learning Sets of Rules – Introduction, sequential covering algorithms, learning rule sets: summary, learning First-Order rules, learning sets of First-Order rules: FOIL, Induction as inverted deduction, inverting resolution. Reinforcement Learning – Introduction, the learning task, Q-learning, non-deterministic, rewards and actions, temporal difference learning, generalizing from examples, relationship to dynamic programming.
Unit – V
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Text Books:
- Machine Learning – Tom M. Mitchell, – MGH
Reference Books:
For the complete syllabus, results, class timetable, and many other features kindly download the iStudy App
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For detail syllabus of all other subjects of B.Tech Electronics & Instrumentation Engineering 4th Year 2nd Sem , visit EIE 4th Year 2nd Sem syllabus subjects.
For B.Tech Electronics & Instrumentation Engineering (EIE) 4th Year results, visit JNTUH B.Tech Electronics & Instrumentation Engineering semester results direct link.