UNIT 1:
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Machine Learning – perspective -Issues
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Examples of Machine Learning Applications
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Types of Machine Learning –Machine Learning process- preliminaries, testing
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Machine Learning algorithms
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Turning data into Probabilities, and Statistics for Machine Learning
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Probability theory -Bayesian Decision Theory.
UNIT 2:
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Subset Selection
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Introduction - Linear Models for Regression – Linear Regression Models and Least Squares
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Shrinkage Methods – Derived Input Directions
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Linear Models for Classification- Discriminant Analysis
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Logistic Regression
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Separating Hyperplanes
UNIT 3:
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Boosting and Additive Trees – Boosting Trees – Regularization – Interpretation – Illustrations
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Neural Networks – Fitting Neural Network - Bayesian Neural Net
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Neural Network Representation – Problems – Perceptron
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Back Propagation Algorithms
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Multilayer Networks
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Case Study: Handwriting Recognition
