Course Contents:
Module I: Supervised Learning: Basic methods: Distance-based methods, Nearest-Neighbors, Decision Trees, Naive Bayes. Linear models: Linear Regression, Logistic Regression, Generalized Linear Models, Support Vector Machines, Nonlinearity and Kernel Methods, Beyond Binary Classification: Multi-class/Structured Outputs. Dimensionality Reduction: Principal Component Analysis. [20 L]
Module II: Artificial Neural Network: Biological neurons and artificial neural network. Learning Methods: Mc-pitt, Hebb’s learning, Perceptron, Adaline and Madaline networks, single layer network, Multilayer feed-forward network, Back-propagation network. [18 L]
Module III: Scalable and advanced Machine Learning: Class Imbalance Problem, Online and Distributed Learning, Semi-supervised Learning, Active Learning, Reinforcement Learning. [12 L]
Module IV: Recent trends in various learning techniques: Recurrence Neural Networks, Convolution Neural Networks, Long Short Term Memory Networks. [10 L]
- Teacher: Dr. Bachchu Paul