Unit - I : Introduction to Machine Learning Introduction : What is Machine Learning, Examples of Machine Learning applications, Training Versus Testing, Positive and Negative Class, Cross - Validation. Types of Learning : Supervised, Unsupervised and Semi - Supervised Learning. Dimensionality Reduction : Introduction to Dimensionality Reduction, Subset Selection, Introduction to Principal Component Analysis. (Chapter - 1) Unit - II : Classification Binary and Multiclass Classification : Assessing Classification Performance, Handling more than two classes, Multiclass Classification - One vs One, One vs Rest Linear Models : Perceptron, Support Vector Machines (SVM), Soft Margin SVM, Kernel methods for non - linearity. (Chapter - 2) Unit - III : Regression and Generalization Regression : Assessing performance of Regression - Error measures, Overfitting and Underfitting, Catalysts for Overfitting, VC Dimensions. Linear Models : Least Square Method, Univariate Regression, Multivariate Linear Regression, Regularized Regression - Ridge Regression and Lasso. Theory of Generalization : Bias and Variance Dilemma, Training and Testing Curves Case Study of Polynomial Curve Fitting. (Chapter - 3) Unit - IV : Logic Based and Algebraic Models Distance Based Models : Neighbors and Examples, Nearest Neighbor Classification, Distance based clustering algorithms - K - means and K - medoids, Hierarchical clustering. Rule Based Models : Rule learning for subgroup discovery, Association rules mining - Apriori Algorithm, Confidence and Support parameters. Tree Based Models : Decision Trees, Minority Class, Impurity Measures - Gini Index and Entropy, Best Split. (Chapter - 4) Unit - V : Probabilistic Models Conditional Probability, Joint Probability, Probability Density Function, Normal Distribution and its Geometric Interpretation, Naïve Bayes Classifier, Discriminative Learning with Maximum Likelihood. Probabilistic Models with Hidden Variables : Expectation - Maximization methods, Gaussian Mixtures. (Chapter - 5) Unit - VI : Trends in Machine Learning Ensemble Learning : Combining Multiple Models, Bagging, Randomization, Boosting, Stacking Reinforcement Learning : Exploration, Exploitation, Rewards, Penalties. Deep Learning : The Neuron, Expressing Linear Perceptron as Neurons, Feed Forward Neural Networks, Linear Neurons and their Limitations, Sigmoid, Tanh and ReLU Neurons. (Chapter - 6)