FUNDAMENTALS OF MACHINE LEARNING - Syllabus

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 FUNDAMENTALS OF MACHINE LEARNING

(Professional Elective - 3)

Department : IT

IV B.Tech I Semester

Course Code

Hours/Week

Credits

Marks

A2275E9

L

T

P

C

CIE

SEE

Total

3

0

0

3

40

60

100

 

Course Outcomes: At the end of the course, the student should be able to

  • 1.     Ability to identify machine learning problems corresponding to different applications.
  • 2.     Ability to understand the basic concepts such as Decision trees and Neural Networks.
  • 3.     Apply Machine Learning algorithms to solve problems of moderate complexity.
  • 4.     Identify Machine Learning applications
  • 5.     Understand Genetic algorithms and their applications

Unit – I

Introduction and Concept Learning: Well-posed learning problems, designing a learning system Perspectives and issues in machine learning, Types of learning. Concept learning task, Concept Learning as search through a hypothesis space, Finding maximally specific hypotheses, Version spaces and the candidate elimination algorithm, Inductive Bias

Unit - II

Decision Tree learning and Neural Network learning: Decision Tree representation and learning algorithm, appropriate problems for Decision Tree Learning, Hypothesis space search in Decision Tree Learning, Inductive bias in Decision Tree Learning: Occam’s razor, Issues in Decision Tree Learning. Introduction to Neural Network Learning, Introduction to Perceptions, Perceptions, Introduction to Gradient Descent and Back propagation.

Unit - III

Support Vector Machines: Introduction, Linear Classifier, Non-linear Classifier, Training SVM, Support Vector Regression.

Bayesian Learning: Bayes theorem and concept learning, Minimum Description Length Principle, Bayes optimal classifier, Gibbs Algorithm, Naïve Bayes Classifier, The EM algorithm.

Unit – IV

Instance-based Techniques: Introduction to Instance-based Techniques, Lazy vs. eager generalization, K nearest neighbor, Radial Basis Function, Case Based reasoning, Remarks on Lazy vs Eager learning.

Unit – V

Genetic Algorithm: Biological motivation, Representing Hypothesis, Genetic Operators, Fitness function and selection, Hypothesis space search, Genetic Programming, Models of Evolution and Learning, Parallelizing Genetic Algorithms.

Text Books:

  1. Machine Learning - Mc Graw Hill, Tom M. Mitchell.

Reference Books:

1.     Machine Learning Methods and the Environmental Science, Neural Network, William W Hsieh Cambridge University Press.

2.     Rechard o Duda, Peter E. Hart and David G. Stork, & pattern Classification, John Wiley & Sons Inc,2001

3.     Chris Bishop, Neural Network for, Pattern Recognition, Oxford University Press. 1995

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