Sunday, September 30, 2018

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.


Kernel Machines- Support Vector Machine- Optimal Separating hyper plane, Soft-margin hyperplane, Kernel trick, Kernel functions. Discrete Markov Processes, Hidden Markov models, Three basic problems of HMMs- Evaluation problem, finding state sequence, Learning model parameters. Combining multiple learners, Ways to achieve diversity, Model combination schemes, Voting, Bagging, Booting.

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


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