# KTU B.Tech S7 Lecture notes Machine Learning

*KTU B.Tech S7 Lecture Notes Machine Learning***Module-1**

Introduction to Machine Learning, Examples of Machine

Learning applications - Learning associations, Classification,

Regression, Unsupervised Learning, Reinforcement Learning.

Supervised learning- Input representation, Hypothesis class,

Version space, Vapnik-Chervonenkis (VC) Dimension

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**Module-2**

Probably Approximately Learning (PAC), Noise, Learning

Multiple classes, Model Selection and Generalization,

Dimensionality reduction- Subset selection, Principle

Component Analysis

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*Classification- Cross validation and re-sampling methods- K- fold cross validation, Boot strapping, Measuring classifier performance- Precision, recall, ROC curves. Bayes Theorem, Bayesian classifier, Maximum Likelihood estimation, Density functions, Regression.*

Decision Trees- Entropy, Information Gain, Tree construction, ID3, Issues in Decision Tree learning- Avoiding Over-fitting, Reduced Error Pruning, The problem of Missing Attributes, Gain Ratio, Classification by Regression (CART), Neural Networks- The Perceptron, Activation Functions, Training Feed Forward Network by Back Propagation.

Unsupervised Learning - Clustering Methods - K-means, Expectation-Maximization Algorithm, Hierarchical Clustering Methods , Density based clustering.

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