UNIT 1:
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Components of learning
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Learning models: Geometric models –Probabilistic models –Logic models
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Types of Learning – Supervised –Unsupervised – Reinforcement
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Theory of learning –Feasibility of learning
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Theory of Generalization –Generalization bound
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Bias and variance –Learning Curve
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Grouping and grading - Learning versus Design
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Error and noise - Training versus Testing
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Error and noise - Training versus Testing
UNIT 2:
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Linear classification – Univariate Linear Regression
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Support Vector Machines- Soft margin SVM
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Perceptrons - Multilayer Neural Networks
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Learning Neural Networks structures
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Logistic regression
UNIT 3:
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K-means – Clustering around Medoids- Silhouttes
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Nearest neighbor models
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Hierarchical clustering
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k-d trees –Locality Sensitive Hashing–
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Ensemble learning
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Bagging and random forests
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Boosting –meta learning
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K-means – Clustering around Medoids- Silhouttes
