JNTUH B.Tech 4th Year 1 sem Computer Science and Engineering R13 (4-1) Machine Learning (Elective – II) R13 syllabus.

JNTUH B.Tech 4th year (4-1) Machine Learning gives you detail information of Machine Learning (Elective – II) 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. Mc Graw Hill. 1997
• Trevor Hus tie, Robert Tibshirani & Jerome Friedman. The Elements of  Statically  Learning, Springer Veriag 2001

REFERENCE BOOKS

• Machine Learning Methods en the Environmental Science, Neural Network, William W Hsieh Cambridge  University Press.
• Rbchard o Duda, Peter E. Hart and David G. Stork, & pattern Classification,.John Wiley & Sons Inc,2001
• Chris Bishop, Neural  Network for, Pattern Recognition, Oxford University Press. 1995

Outcomes

• Student Should be we to understand the basic concepts such decision tree and  neural networks.
• Ability to formulate machine learning techniques to respective problems.
• Apply  machine learning  algorithms to solve problems of moderate complexity.

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