Machine Learning syllabus for MCA 2nd Year 1st Sem R22 regulation JNTUH

Machine Learning detailed syllabus for Master of Computer Applications(MCA), R22 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 MCA 2nd Year 1st Sem syllabus for R22 regulation JNTUH, do visit MCA 2nd Year 1st Sem syllabus for R22 regulation JNTUH subjects. The detailed syllabus for machine learning is as follows.

Course Objectives:

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Course Outcomes:

Upon completion of the course, the students will be able to:

Distinguish between, supervised, unsupervised and semi-supervised learning
Apply the apt machine learning strategy for any given problem
Suggest supervised, unsupervised or semi-supervised learning algorithms for any given problem
Design systems that use the appropriate graph models of machine learning
Modify existing machine learning algorithms to improve classification efficiency

Unit I

Introduction:

Unit II

Linear Models: Multi-layer Perceptron- Going Forwards – Going Backwards: Back Propagation Error – Multi-layer Perceptron in Practice – Examples of using the MLP – Overview – Deriving Back-Propagation – Radial Basis Functions and Splines – Concepts – RBF Network – Curse of Dimensionality – Interpolations and Basis Functions – Support Vector Machines

Unit III

Tree and Probabilistic Models: Learning with Trees – Decision Trees – Constructing Decision Trees – Classification and Regression Trees – Ensemble Learning – Boosting – Bagging – Different ways to Combine Classifiers – Basic Statistics -Gaussian Mixture Models – Nearest Neighbor Methods – Unsupervised Learning – K means Algorithms

Unit IV

Unit V

Graphical Models: Markov Chain Monte Carlo Methods – Sampling – Proposal Distribution – Markov Chain Monte Carlo -Graphical Models – Bayesian Networks – Markov Random Fields – Hidden Markov Models – Tracking Methods

Text Books:

Stephen Marsland, ‘Machine Learning – An Algorithmic Perspective, Second Edition, Chapman and Hall/ CRC Machine Learning and Pattern Recognition Series, 2014.
Tom M Mitchell, ‘Machine Learning, First Edition, McGraw Hill Education, 2013.

Reference Books:

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

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