CS601PC: Machine Learning CSE Syllabus for B.Tech 3rd Year 2nd Sem R18 Regulation JNTUH

Machine Learning detailed Syllabus for Computer Science & Engineering (CSE), R18 regulation has been taken from the JNTUH official website and presented for the students affiliated to JNTUH course structure. For Course Code, Subject Names, 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 other CSE 3rd Year 2nd Sem Syllabus for B.Tech R18 Regulation JNTUH, do visit CSE 3rd Year 2nd Sem Syllabus for B.Tech R18 Regulation JNTUH Subjects. The detailed Syllabus for machine learning is as follows.

Pre-requisite:

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Course Objectives:

This course explains machine learning techniques such as decision tree learning, Bayesian learning etc.
To understand computational learning theory.
To study the pattern comparison techniques.

Course Outcomes:

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

Unit II

Artificial Neural Networks-1- Introduction, neural network representation, appropriate problems for neural network learning, perceptions, multilayer networks and the back-propagation algorithm.

Artificial Neural Networks-2- Remarks on the Back-Propagation algorithm, An illustrative example: face recognition, advanced topics in artificial neural networks.

Evaluation Hypotheses – Motivation, estimation hypothesis accuracy, basics of sampling theory, a general approach for deriving confidence intervals, difference in error of two hypotheses, comparing learning algorithms.

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

Unit V

Analytical Learning-1- Introduction, learning with perfect domain theories: PROLOG-EBG, remarks on explanation-based learning, explanation-based learning of search control knowledge.

Analytical Learning-2-Using prior knowledge to alter the search objective, using prior knowledge to augment search operators.

Combining Inductive and Analytical Learning – Motivation, inductive-analytical approaches to learning, using prior knowledge to initialize the hypothesis.

Text Books:

Machine Learning – Tom M. Mitchell, – MGH

Reference Books:

For detail Syllabus of all other subjects of B.Tech 3rd Year Computer Science & Engineering, visit CSE 3rd Year Syllabus Subjects.

For all B.Tech results, visit JNTUH B.Tech all years, and semester results from direct links.

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