UNIT 1:
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Definition of learning systems Goals and applications of machine learning
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Types of Machine Learning Machine Learning Process
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Testing Machine Learning Algorithms
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Terminology-Weight Space
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The Curse of Dimensionality
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Testing Machine Learning Algorithms
UNIT 2:
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Regression: Linear Regression
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Parametric Models- Multivariate Regression Regression:
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Classification: Bayesian Decision Theory parametric and non-parametric methods
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Multivariate Classification Logistic Regression
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K-Nearest Neighbor classifier
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Decision Tree based methods for classification and Regression Ensemble methods
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Decision Tree based methods for classification and Regression Ensemble methods
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Decision Tree based methods for classification and Regression Ensemble methods
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Classification: Bayesian Decision Theory parametric and non-parametric methods
UNIT 3:
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Clustering-K-means clustering
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Principal Component Analysis
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Hierarchical Clustering
UNIT 4:
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The Brain and The Neuron
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Perceptron-Training the perceptron
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Perceptron Learning Algorithm
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Multilayer Perceptron
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Back Propagation Dimensionality Reduction
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Back Propagation Dimensionality Reduction
UNIT 5:
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Convolutional Networks
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Recurrent Neural Networks
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Bidirectional RNNs
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Deep Recurrent Networks
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Recursive Neural Networks
