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Introduction To Machine Learning
The goal of machine learning is to program computers to optimize a performance criterion using example data or past experience. Many successful applications of machine learning exist already, including systems that analyze past sales data to predict consumer behaviour, recognize faces or spoken spee...
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| Format: | Printed Book |
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Prenice- Hall of India
2005
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Table of Contents:
- Supervised learning
- Bayesian decision theory
- Parametric methods
- Multivariate methods
- Dimensionality reduction
- Clustering
- Nonparametric methods
- Decision trees
- Linear discrimination
- Multilayer perceptrons
- Local models
- Hidden Markov models
- Assessing and comparing classification algorithms
- Combining multiple learners
- Reinforcement learning
- Probability.