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Course Detail

Course Name Machine Learning and Data Mining
Course Code 15CSE401
Program B. Tech. in Computer Science and Engineering
Semester Seven
Year Taught 2019


Unit 1

Introduction to Machine learning: Supervised learning, Unsupervised learning, some basic concepts in machine learning, Review of probability, Computational Learning theory. Bayesian concept learning, Likelihood, Posterior predictive distribution, Naive Bayes classi?ers, The log-sum-exp trick, Feature selection using mutual information, Linear Regression, Logistic regression.

Unit 2

Introduction to data mining – challenges and tasks, measures of similarity and dissimilarity, Classification – Rule based classifier, Nearest – neighbour classifiers -Bayesian classifiers – decision trees; support vector machines, Class imbalance problem performance evaluation of the classifier, comparison of different classifiers.

Unit 3

Association analysis – frequent item generation rule generation, evaluation of association patterns. Cluster analysis, K means algorithm, cluster evaluation, application of data mining to web mining and Bioinformatics. Classifying documents using bag of words advertising on the Web, Recommendation Systems, and Mining Social network graphs.

Text Books

  1. Kevin P. Murphey, “Machine Learning, a probabilistic perspective”, The MIT Press, 2012.
  2. Jiawei Han and MichelineKamber, Jian Pei, “Data Mining: Concepts and Techniques”, Third Edition, Elsevier, 2012.


  • Pang-Ning Tan, Michael Steinbach and Vipin Kumar, “Introduction to Data Mining”, First Edition, Pearson Education, 2006.
  • Tom Mitchael, “Machine Learning”, McGraw Hill, 1997

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