UNIT 1:
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Machine Learning – perspective – Issues - Examples of Machine Learning Applications
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Machine Learning – perspective – Issues - Examples of Machine Learning Applications
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turning data into Probabilities, and Statistics for Machine Learning
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Probability theory -Bayesian Decision Theory.
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Machine Learning process- preliminaries, testing Machine Learning algorithms
UNIT 2:
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Linear models and least squares
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Subset selection
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Linear Models for Classification- Discriminant Analysis
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Shrinkage Methods – Derived Input Directions
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Logistic Regression – Separating Hyper planes
UNIT 3:
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Neural Networks and fitting
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Bayesian Neural Net
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Interpretation
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Neural Network Representation
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Regularization
UNIT 4:
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Guassian mixtures
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Independent component Analysis
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K- means clustering
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Self organizing maps
UNIT 5:
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Generalization
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Model based learning
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Reinforcement Learning
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temporal difference learning
