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
UNIT-III
Some Basic Statistics: Averages, Variance and Covariance, The Gaussian, The Bias-Variance Trade-off
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
Suggested Readings:
1. Tom M. Mitchell, Machine Learning, Mc Graw Hill, 1997
2. Stephen Marsland, Machine Learning – An Algorithmic Perspective, CRC Press, 2009
3. Margaret H Dunham, Data Mining, Pearson Edition., 2003.
Faculty of Engineering, O.U CBCS Curriculum with effect from Academic Year 2019 – 2020
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4. Galit Shmueli, Nitin R Patel, Peter C Bruce, Data Mining for Business Intelligence, Wiley India
Edition, 2007
5. Rajjan Shinghal, Pattern Recognition, Oxford University Press, 2006.