1.Introduction to Machine Learning Introduction, Different Types of Learning, Hypothesis Space, Inductive Bias, Evaluation and Cross Validation (Chapter - 1) 2.Basic Machine Learning Algorithms Linear Regression, Decision Trees, Learning Decision Trees, K-nearest Neighbour, Collaborative Filtering, Overfitting (Chapter - 2) 3.Dimensionality Reduction Feature Selection, Feature Extraction (Chapter - 3) 4.Bayesian Concept of Learning Bayesian Learning, Naïve Bayes, Bayesian Network, Exercise on Naïve Bayes (Chapter - 4) 5.Logistic Regression and Support Vector Machine Logistic Regression, Introduction to Support Vector Machine, The Dual Formation, Maximum Margin with Noise, Nonlinear SVM and Kernel Function, SVM: Solution to the Dual Problem (Chapter - 5) 6.Basics of Neural Network Introduction to neural network, Multilayer Neural Network, Neural Network and Backpropagation Algorithm, Deep Neural Network (Chapter - 6) 7.Computation and Ensemble Learning Introduction to Computation Learning, Sample Complexity: Finite Hypothesis Space, VC Dimension, Introduction to Ensembles, Bagging and Boosting (Chapter - 7) 8.Basic Concepts of Clustering Introduction to Clustering, K-means Clustering, Agglomerative Hierarchical Clustering (Chapter - 8)
