UNIT– I Supervised Learning: Learning a Class from Examples, Vapnik Chervonenkis (VC) Dimension, Probably Approximately Correct (PAC) Learning, Noise, Learning Multiple Classes, Regression, Model Selection and Generalization, Dimensions of a Supervised Machine Learning Algorithm Bayesian Decision Theory: Classification, Losses and Risks, Discriminant Functions, Utility Theory, Association Rules
UNIT–II Parametric Methods: Maximum Likelihood Estimation, Evaluating an Estimator: Bias and Variance, The Bayes' Estimator, Parametric Classification, Regression, Tuning Model Complexity: Bias/Variance Dilemma, Model Selection Procedures
UNIT–III Dimensionality Reduction: Subset Selection, Principal Components Analysis, Factor Analysis, Multidimensional Scaling, Linear Discriminant Analysis Association learning: Basics of Association, Apriori Algorithm, Eclat Algorithm, FP Growth Algorithm with examples, SCADA application with FP Growth Algorithm
UNIT-IV Unsupervised Learning: Expectation Maximization, Self-Organizing Maps(SOM),learning Process in SOM, Algorithm: SOM, Adaptive Resonance Theory. Clustering: k-Means Clustering, Expectation-Maximization Algorithm, Supervised Learning after Clustering,Fuzzy Clustering, Document Clustering example, Hierarchical Clustering, Choosing the Number of Clusters
UNIT-V Decision Trees: Univariate Trees, Pruning, Rule Extraction from Trees, Learning Rules from Data. Random Forest: basic Principle, Decision Tree vs random Forest, Random Forest Algorithm with Example
