mayank1101 / Pattern-Recognition-and-Machine-Learning-PRML-

M.Tech 2nd Semester

Pattern Recognition and Machine Learning (PRML) [Subject Code - IT-542]

M.Tech 2nd Semester

In This Subject following topics were covered : -

• Multi Variate Normal Distribution.
• Maximum Likelihood Estimation.
• Bayesian Classifier.
• Bayes decision rule under normality assumption.
• Parameter Estimation.
• KNN desnity estimation procedure.
• Naive Bayes Classifier.
• Unsupevised Classification.
• Minimum within cluster distance.
• K Means Algorithm.
• Fuzzy K Means Algorithm.
• Kernal Density Estimation.
• Principle Component Analysis.
• Non Linear Dimensionality Reduction.
• Regression
• Logistic Regression
• Gaussian Mixture Model
• Expectation Maximization Algorithm (EM Algorithm)
• Support Vector Machines

Note: Above mentioned topics are not in the order in which they were covered

• Book Used for Reference

• Bishop - Pattern Recognition And Machine Learning

• NPTEL Video Lectures for Reference

• Computer Science - Pattern Recognition by CA Murthy

• For Lab Practical please refer Lab directory

• Lab Directory