MCA

Machine Learning syllabus for MCA 2nd Year 2nd Sem R20 regulation JNTUH (Professional Elective-3)

Machine Learning detailed syllabus for Master of Computer Applications (MCA), R20 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 the other MCA 2nd Year 2nd Sem syllabus for R20 regulation JNTUH, visit Master of Computer Applications 2nd Year 2nd Sem R20 Scheme.

For all the (Professional Elective-3) subjects refer to Professional Elective-3 Scheme. The detail syllabus for machine learning is as follows.

Prerequisites:

For the complete syllabus, results, class timetable, and many other features kindly download the iStudy App
It is a lightweight, easy to use, no images, and no pdfs platform to make students’s lives easier.
Get it on Google Play.

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

For the complete syllabus, results, class timetable, and many other features kindly download the iStudy App
It is a lightweight, easy to use, no images, and no pdfs platform to make students’s lives easier.
Get it on Google Play.

Unit -II

Artificial Neural Networks: 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

For the complete syllabus, results, class timetable, and many other features kindly download the iStudy App
It is a lightweight, easy to use, no images, and no pdfs platform to make students’s lives easier.
Get it on Google Play.

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:

  1. Machine Learning – Tom M. Mitchell, – MGH.

Reference Books:

For the complete syllabus, results, class timetable, and many other features kindly download the iStudy App
It is a lightweight, easy to use, no images, and no pdfs platform to make students’s lives easier.
Get it on Google Play.

For detail syllabus of all other subjects of 2nd Year 2nd Sem Master of Computer Applications, visit MCA 2nd Year 2nd Sem syllabus subjects.

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

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