JNTUH B.Tech 4th Year 2 sem Information Technology R13 (4-2) Machine Learning (Elective -IV) R13 syllabus.

JNTUH B.Tech 4th year (4-2) Machine Learning gives you detail information of Machine Learning (Elective -IV) R13 syllabus It will be help full to understand you complete curriculum of the year.

Objectives

• To be able to formulate machine learning problems corresponding to different applications.
• To understand a range of machine learning algorithms along with their strengths and weaknesses.
• To understand the basic theory underlying machine learning.

UNIT-I

Introduction: An illustrative learning task, and a few approaches to it. What is known from algorithms? Theory, Experiment. Biology. Psychology.

Concept Learning: Version spaces. Inductive Bias. Active queries. Mistake bound! PAC model. basic results. Overview of issues regarding data sources, success criteria.

UNIT —II

Decision Tree Learning: – Minimum Description Length Principle. Occam’s razor. Learning with active queries

Neural Network Learning: Perceptions and gradient descent back propagation.

UNIT —III

Sample Complexity and Over fitting: Errors in estimating means. Cross Validation and jackknifing VC dimension.

Irrelevant features: Multiplicative rules for weight tuning.

Bayesian Approaches: The basics Expectation Maximization. Hidden Markov Models.

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TEXT BOOKS

• Tom Michel, Machine Learning, McGraw Hill, 1997
• Trevor Has tie, Robert Tibshirani & Jerome Friedman. The Elements
• of Statically Learning, Springer Verlag, 2001.

REFERENCE BOOKS

• Machine Learning Methods in the Environmental Sciences, Neural Networks, William W Hsieh, Cambridge Univ Press.
• Richard o. Duda, Peter E. Hart and David G. Stork, pattern classification, John Wiley & Sons Inc., 2001.
• Chris Bishop, Neural Networks for Pattern Recognition, Oxford University Press, 1995.

Outcomes

• Student should be able to understand the basic concepts such as decision trees and neural networks.
• Ability to formulate machine learning techniques to respective problems.
• Apply machine learning algorithms to solve problems of moderate corn plexity.

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