Unit I : Introduction to Machine Learning Why Machine learning ? Types of machine learning, basic concepts in machine learning like parametric and non-parametric modeling, linear and nonlinear regression, overfitting and dimensionality reduction. Decision trees, Feature reduction. (Chapter - 1) Unit II : Models for Regression and Classification Linear Models for Regression : Least Squares and Nearest Neighbors, Linear Basis Function Models, The Bias-Variance Decomposition, Bayesian Linear Regression, Bayesian Model Comparison Linear Models for Classification : Discriminant Functions. Probabilistic Discriminative Models Multivariate Data, Parameter Estimation, Multivariate Classification, Multivariate Regression Kernal Methods : Support Vector machines and Relevance Vector Machines. (Chapter - 2) Unit III : Clustering Dimensionality Reduction : Principal Components Analysis, Factor Analysis, Multidimensional Scaling, Linear Discriminant Analysis Clustering : k-Means Clustering, Mixtures of Gaussians. (Chapter - 3) Unit IV : Artificial Neural Networks I Biological neuron, Artificial neuron model, concept of bias and threshold, Activation functions, McCulloch-Pits Neuron Model, learning paradigms, concept of error energy, gradient descent algorithm and application of linear neuron for linear regression, : Learning mechanisms : Hebbian, Delta Rule, Perceptron and its limitations. (Chapter - 4) Unit V : Artificial Neural Networks II Multilayer perceptron (MLP) and back propagation algorithm, Application of MLP for classification, Self-Organizing Feature Maps, Learning vector quantization Radial Basis Function networks. (Chapter - 5) Unit VI : Deep Learning and Convolution Neural Networks Improvement of the Deep Neural Network : Vanishing Gradient, Overfitting, Computational Load, ReLU Function, Dropout Architecture of ConvNet, Convolution Layer, Pooling Layer, Applications of CNN's. (Chapter - 6)
