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.
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.
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.