Download Machine Learning

With effect from Academic Year 2016-17
Course Code 16ITE122
MACHINE LEARNING
(Elective-II)
Instruction
Duration of End Examination
3L per week
3 Hours
End Examination
70 Marks
Sessional
30 Marks
Credits
3
Course Prerequisites: Discrete Mathematics, Probability and Random Theory
Course Objectives:
1. To Learn the concepts of linear classification and nonlinear classification
2. Understand the mathematical concepts related to Multilayer perception.
3. To develop an understanding of the clustering techniques
Course Outcomes:
1. Acquire the basic knowledge of Machine Learning, identify algorithms, machine learning
problems.
2. Ability to classify data sets using nonlinear classifiers.
3. To be familiar with Linear Regression Techniques.
4. Able to recognize patterns using graphical models.
5. Ability to apply dimensionality reduction techniques on datasets.
6. Capacity to apply clustering techniques.
UNIT-I
Introduction: Learning, Types of Machine Learning.
Concept learning: Introduction, Version Spaces and the Candidate Elimination Algorithm.
Learning with Trees: Constructing Decision Trees, CART, Classification Example.
UNIT-II
Linear Discriminants: The Perceptron, Linear Separability.
Linear Regression Multilayer Perceptron (MLP): Going Forwards, Backwards, MLP in
practices, Deriving back.
Propagation SUPPORT Vector Machines: Optimal Separation, Kernels.
With effect from Academic Year 2016-17
UNIT-III
Some Basic Statistics: Averages, Variance and Covariance, The Gaussian.
The Bias-Variance Tradeoff Bayesian learning: Introduction, Bayes theorem, Bayes Optimal
Classifier, Naive Bayes Classifier.
Graphical Models: Bayesian networks, Approximate Inference, Making Bayesian Networks,
Hidden Markov Models, The Forward Algorithm.
UNIT-IV
Evolutionary Learning: Genetic Algorithms, Genetic Operators.
Genetic Programming Ensemble learning: Boosting, Bagging.
Dimensionality Reduction: Linear Discriminant Analysis, Principal Component Analysis
UNIT-V
Clustering: Introduction, Similarity and Distance Measures, Outliers, Hierarchical Methods,
Partitional Algorithms, Clustering Large Databases, Clustering with Categorical Attributes,
Comparison.
Text Book:
1. Tom M. Mitchell, "Machine Leaming ",MacGraw Hill, 1997
References:
1. Stephen Marsland, "Machine Learning - An Algorithmic Perspective ", CRC Press, 2009.
2. Margaret H Dunham, "Data Mining", Pearson Edition, 2003.
3. GalitShmueli, Nitin R Patel, Peter C Bruce, "Data Mining for Business Intelligence", Wiley
India Edition, 2007.
4. RajjallShinghal, "Pattern Recognition ", Oxford University Press, 2006.