Probability concepts, Probability simulations, Sampling concepts – random sampling, sampling distribution-, Parameter estimation methods – Maximum Likelihood Estimation, Method of Moments- Random number generation – General techniques for generating Random Variables, Monte Carlo Algorithms-Buffon’s needle experiment,
Monte carlo integration, Monte Carlo Methods for Inferential Statistics – Monte Carlo Hypothesis Testing, Bootstrap Methods – Exploratory data analysis – Traditional statistics methods and computational statistics methods , Frequentist statistics and Bayesian statistics Linear models and regression analysis – Maximum likelihood estimation, Linear Regression, Polynomial Regression, Stepwise Regression, Ridge Regression, Lasso, ElasticNet – Statistical Pattern Recognition- Bayes Decision Theory Estimating Class-Conditional Probabilities, Bayes Decision Rule Classification and Regression Trees, Clustering
Classification trees, Algorithm for Normal Attributes, Information Theory and Information. Entropy, Highly-Branching Attributes, ID3 to c4.5, CHAID, CART, Regression Trees, Model Trees, Pruning. Preprocessing and Post processing in data mining – Steps in Preprocessing, Discretization, Manual Approach, Binning, Entropy- based Discretization, Gaussian Approximation, K-tile method, Chi Merge, Feature extraction, selection and construction, Feature extraction, Algorithms, Feature selection, Feature construction, Missing Data, Post processing.